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We develop a Transformer-based model enhanced with a Channel Attention Module (CAM) to capture the inter-channel dependencies in 3D hyperspectral point cloud data for geological applications. We hypothesize that specific channels of hyperspectral data correspond to distinct mineral types, and therefore, exploiting the relationships among these channels is beneficial for our analysis. We evaluate our method using the newly released Tinto dataset, which consists of 3D hyperspectral point clouds featuring three different spectral ranges: LongWave Infrared (LWIR), ShortWave Infrared (SWIR), and Visible-Near Infrared (VNIR).We explore four different CAMs from various networks—SENet, ECANet, CBAM, and DANet—and successfully integrate them into a CNN-based model to enhance feature representation. We specifically tailor the channel attention to our use of 3D hyperspectral point cloud data. Our experiments demonstrate significant improvements in performance after incorporating the CAM into our backbone model, which draws inspiration from the Point Cloud Transformer architecture and Vector Self-Attention mechanism. These results highlight the potential for further research into enhancing classification accuracy using hyperspectral data in geological applications. The code will be released on https://github.com/aldinorizaldy/CAM-Transformer. [ABSTRACT FROM AUTHOR]

Developing a precise tree stem curve and robust estimation of stem volume are crucial for forest inventories with various applications. Laser scanned point clouds have been recognized as the most practical data for tree information modeling. Many methods for stem curve development involve estimating stem diameters at different heights and determining stem volume by utilizing fitted cylinders based on these diameters and the associated heights. The estimation of diameter depends on circle fitting. However, many circle fitting methods are non-robust and inaccurate in the presence of noise, outliers, and significant data gaps, resulting in faulty diameters and imprecise stem volume. Limited scanning, occlusions from the physical complexity, high tree density, and adjacent branches may cause data incompleteness, and generate outliers. To address these challenges, we employ robust statistical approaches to restrain the influence of outliers and data gaps. This paper contributes by (i) exploring the problems of robust diameter estimation for partial data, and in the presence of noise and outliers, (ii) understanding the impacts of using erroneous diameters in cylinder fitting, and later for stem curve and volume estimation, and (iii) developing a robust method that couples robust circle and cylinder fittings to derive precise stem curve and estimation of stem volume in the presence of outliers and partial data. We demonstrate the performance of the proposed algorithm through terrestrial laser scanning point clouds. [ABSTRACT FROM AUTHOR]

Porches, as defined by the Art & Architecture Thesaurus, serve as vital transitional spaces linking indoor and outdoor environments. Despite their historical and contemporary significance, porches lack explicit representation in prevalent standards like CityGML and IndoorGML, posing challenges for comprehensive spatial modeling and its application. This paper proposes a method for modeling porches that aligns with the existing OGC standard CityGML 3.0, ensuring accuracy and compatibility. Drawing upon geomatics techniques, the method aims to bridge the gap in representing these spaces, critical for applications such as navigation systems, urban planning, and energy simulations. By integrating geometric, machine learning, and informative modeling approaches, this method seeks to provide a robust foundation for various practical applications. The paper outlines a comprehensive state-of-the-art review, describes the proposed method from digitalization to random forest (RF)-based point cloud classification and vectorization, presents case studies and results, and offers critical discussions and conclusions. Through this endeavor, the paper contributes to enhancing the representation and understanding of porches within the digital spatial landscape. [ABSTRACT FROM AUTHOR]

Nowadays, there are many publicly available georeferenced data, like 3D CityGML models, that can be used as prior knowledge to perform accurate global localization. Iterative Closest Point (ICP) is a promising method for achieving this task, but it requires two point clouds that need to be partially overlapping in the initial state for better registration performance. Therefore, we investigated different detection and matching methods to automatically pre-register two non-overlapping point clouds based on a 2D overhead view and evaluated the registration results produced by an ICP algorithm. We used public data from the city of Aachen, Germany. A georeferenced point cloud was derived from the LOD2 CityGML model and a local point cloud was reconstructed from an image sequence using Structure from Motion (SFM). The evaluation results show that georeferenced LOD2 CityGML models can successfully be used for city-scale sub-meter global localization. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of directly utilizing 3D indoor point clouds for real-time indoor navigation, particularly to enhance emergency response processes. Traditional indoor navigation research primarily focuses on creating navigation systems from pre-existing indoor models, resulting in a graph representation that simplifies spatial relationships, requires post-processing, and delivers results only afterwards, often overlooking real-time obstacles and complex layouts such as those in modern office floors. This research proposes an original approach by leveraging real-time generated 3D models using HoloLens 2 sensors, which combine RGB images and depth sensor output to create a comprehensive point cloud. The study explores path planning directly within these point clouds without the need for extensive preprocessing or segmentation, aiming to provide immediate navigation support with minimal delay. Utilizing the Rapidly Exploring Random Trees (RRT) algorithm, the research seeks to minimize preprocessing and swiftly visualize navigable paths, evaluating the system's performance in terms of processing time and path viability. This approach addresses the limitations of traditional graph-based methods and the challenges posed by outdated or unavailable indoor models, offering a promising solution for real-time emergency navigation assistance. [ABSTRACT FROM AUTHOR]

In recent years, laser scanning and other survey techniques have become popular for documenting and preserving architectural and structural heritage, including masonry bridges. These bridges require careful monitoring to ensure proper structural integrity analyses. However, analysing large datasets of complex point clouds can be challenging and time consuming. This research introduces an automated method to quickly assess the geometric characteristics of vault of bridges by means of a simple algorithm purposely developed for this application. The developed methodology was implemented in MATLAB. The main geometric features, such as length, arrow, and span, are extracted, and conditional algorithms classify the vault types based on specific thresholds. The methodology is tested on surveyed masonry bridges coming from a case study: the historical Circumetnea railway bridges in Sicily, Italy. A digital survey campaign was conducted using various survey techniques such as laser scanning and photogrammetry to obtain the vault point clouds. This research represents a step forward from previous methodologies by efficiently segmenting and analysing vaults from point clouds, enabling accurate classification and geometric analysis. [ABSTRACT FROM AUTHOR]

The eulittoral zone, which alternates between being exposed and submerged, presents a challenge for high-resolution characterization. Normally, its mapping is divided between low and high water levels, where each calls for a different type of surveying instrument. This leads to inconsistent mapping products, both in accuracy and resolution. Recently, uncrewed airborne vehicle (UAV) based photogrammetry was suggested as an available and low-cost solution. However, relying on a passive sensor, this approach requires adequate environmental conditions, while its ability to map inundated regions is limited. Alternatively, UAV-based topo-bathymetric laser scanners enable the acquisition of both submerged and exposed regions independent of lighting conditions while maintaining the acquisition flexibility. In this paper, we evaluate the applicability of such systems in the eulittoral zone. To do so, both topographic and topo-bathymetric LiDAR sensors were loaded on UAVs to map a coastal region along the river Rhein. The resulting point clouds were compared to UAV-based photogrammetric ones. Aspects such as point spacing, absolute accuracy, and vertical offsets were analysed. To provide operative recommendations, each LiDAR scan was acquired at different flying altitudes, while the photogrammetric point clouds were georeferenced based on different exterior information configurations. To assess the riverbed modelling, we compared the surface model acquired by the topo-bathymetric LiDAR sensor to multibeam echosounder measurements. Our analysis shows that the accuracies of the LiDAR point clouds are hardly affected by flying altitude. The derived riverbed elevation, on the other hand, shows a bias which is linearly related to water depth. [ABSTRACT FROM AUTHOR]

The application of Structure-from-Motion (SfM) and Multi-View-Stereo matching with aerial images can be successfully used for deriving dense point clouds to analyse changes in the mountain environment, which is characterized by changes due to the action of natural process. The comparison of multiple datasets requires to setup a stable reference system, task that is generally implemented by means of ground control points (GCPs). On the other hand, their positioning may be sometimes difficult in mountains. To cope with this drawback an approach termed as Multitemporal SfM (MSfM) is presented: multiple blocks are oriented together within a unique SfM project, where GCPs are used in only one epoch for establishing the absolute datum. Accurate coregistration between different epochs depends on the automatic extraction of tie points in stable areas. To verify the application of MSfM in real cases, this paper presents three case studies where different types of photogrammetric data are adopted, including images from drones and manned aircrafts. Applications to glacier and mountain river erosion are entailed. [ABSTRACT FROM AUTHOR]

To solve the problem of collapsing block walls widely used in Japan, this study proposes a method for extracting block walls using 3D point cloud data measured by the Mobile Mapping System (MMS). Unlike conventional methods, this method identifies block walls based on geometric features without relying on MMS trajectory data or deep learning inference results. In addition, the computational load is low and manual correction can be minimized. In our experiments, we used point cloud data collected in urban areas in Japan and achieved a precision of 0.750, recall of 0.810, and F-measure of 0.779. The results demonstrate the effectiveness of this method for automatic extraction of block walls and rapid assessment of collapse risk and are expected to contribute to safety measures in areas with high seismic risk. [ABSTRACT FROM AUTHOR]

Semantic instance segmentation from scenes, serving as a crucial role for 3D modelling and scene understanding. Conducting semantic segmentation before grouping instances is adopted by the existing state-of-the-art methods. However, without additional refinement, semantic errors will fully propagate into the grouping stage, resulting in low overlap with the ground truth instance. Furthermore, the proposed methods focused on indoor level scenes, which are limited when directly applied to large-scale outdoor Airborne Laser Scanning (ALS) point clouds. Numerous instances, significant object density and scale variations make ALS point clouds distinct from indoor data. In order to address the problems, we proposed a geometric characterization-aware semantic instance segmentation network, which utilized both semantic and objectness score to select potential points for grouping. And in point cloud feature learning stage, hand-craft geometry features are taken as input for geometric characterization awareness. Moreover, to address errors propagated from previous modules after grouping, we have additionally designed a per-instance refinement module. To assess semantic instance segmentation, we conducted experiments on an open-source dataset. Additionally, we performed semantic segmentation experiments to evaluate the performance of our proposed point cloud feature learning method. [ABSTRACT FROM AUTHOR]

Wildlife research in both terrestrial and aquatic ecosystems now deploys drone technology for tasks such as monitoring, census counts and habitat analysis. Unlike camera traps, drones offer real-time flexibility for adaptable flight paths and camera views, thus making them ideal for capturing multi-view data on wildlife like zebras or lions. With recent advancements in animals' 3D shape & pose estimation, there is an increasing interest in bringing 3D analysis from ground to sky by means of drones. The paper reports some activities of the EU-funded WildDrone project and performs, for the first time, 3D analyses of animals exploiting oblique drone imagery. Using parametric model fitting, we estimate 3D shape and pose of animals from frames of a monocular RGB video. With the goal of appending metric information to parametric animal models using photogrammetric evidence, we propose a pipeline where we perform a point cloud reconstruction of the scene to scale and localize the animal within the 3D scene. Challenges, planned next steps and future directions are also reported. [ABSTRACT FROM AUTHOR]

If multimedia-photogrammetry is used for the generation of point clouds of submerged objects or of the water bottom, Snell's law has to be considered. When the images are taken from air, image rays are refracted at the air-water interface. This results in the collinearity equations being no longer valid. Bundle block adjustment can still be solved by adding additional terms considering Snell's law. Existing approaches usually assume that the water surface is flat. Refractive indices and water height can either be measured separately or included as unknowns in the adjustment. However, when the water surface is not flat due to the presence of waves, assuming a planar water surface leads to large geometric errors. This work will analyze the significance of those errors and propose a way of including water surface parameters as unknowns into the bundle block adjustment, both based on simulated data. The simulation reproduces multiple images taken simultaneously, e.g. from synchronized UAV cameras or from cameras on tripods. [ABSTRACT FROM AUTHOR]

Clearing and construction activities related to the Maya Train (Tren Maya) project resulted in potential and inevitable impacts to archaeological caves sites in largely undeveloped areas of Quintana Roo. An effort coordinated by Mexico's National Institute of Anthropology and History (INAH) involved accelerated digital documentation of two caves – via SLAM-enabled mobile LiDAR scanning and targeted photogrammetry – to facilitate prompt visualization and evaluation of terrestrial and subterranean geospatial relationships. Mobile LiDAR is well suited to the challenges of capturing the complex, multilevel morphology of caves and was readily deployed across and through priority environments. Specific archaeological features – such as ancient Maya rock art and masonry shrines – were documented via photogrammetry, and the resulting higher-resolution models co-referenced with the georeferenced mobile LiDAR-generated point clouds of each cave and the surrounding topographic context. This integrative approach contributed to a more informed decision-making process, with respect to conservation and construction, and provided baseline data for future monitoring of the affected cave sites. [ABSTRACT FROM AUTHOR]

In Reina del Cisne (Cuenca-Ecuador), a dynamic sliding process occurred due to a cut made at the beginning of 2018 on the hillside without technical considerations for the construction of an access road to a house in the sector. From January 2019 to June 2019, the period analyzed in this work, the landslide caused complete structural damage to dwellings near the hillside and partial damage to houses farther away. It also led to the total collapse of the path that initiated the landslide. Field visits and comparisons using CloudCompare of point clouds obtained from UAV flights between January 2019 and June 2019 highlighted the high activity of this landslide. The analysis demonstrates the effectiveness of this technique for early detection of landslides, enabling timely warnings for inhabitants to take immediate measures to avoid disasters. [ABSTRACT FROM AUTHOR]

Monitoring tree growth processes is relevant for ecological research and understanding the intricate relationship between vegetation and the environment. Time series analyses have revealed a correlation between leaf emergence timing and climate change, with earlier leaf emergence attributed to global warming. While traditional forest inventory methods struggle to quantify growth processes on small scales, terrestrial laser scanning provides a powerful alternative for providing high-resolution 3D information. This study explores the use of high-frequency hyper-temporal terrestrial laser scanning data to quantitatively describe deciduous tree growth, tested on a pedunculate oak (Quercus robur). The research aims to address key questions about detecting leaf growth in hypertemporal terrestrial laser scanning data. Additionally, it explores how 3D tree parameters and point cloud comparisons capture leaf and tree growth throughout the year. Results from M3C2 point cloud analyses indicate that the temporary branch movements correlate with precipitation. Over the year, branch movements were detected to increase with growing distance from the trunk. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop a system to support rapid ground vehicle activities by planning safe travel routes for ground vehicles from point clouds of wide-area uneven terrain environments measured using UAVs. However, fast path planning is difficult in complex environments such as large, uneven terrain environments. Therefore, this paper proposes a new RRT method based on the RRT algorithm that can perform fast path planning, even in complex environments. In the proposed method, narrow areas that are difficult to be explored by ordinary RRTs are first identified in advance, and nodes are placed in these areas to guide the search. When searching with RRTs, the tree is extended via these guide nodes to efficiently traverse the narrow area. In the validation of the proposed method, a comparison was made with RRT and RRT-Connect in two environments, including narrow areas. The results show that the proposed method has a higher route discovery capability, at least two times fewer search nodes and five times faster path planning capability than other RRTs. [ABSTRACT FROM AUTHOR]

A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%. [ABSTRACT FROM AUTHOR]

The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian's Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage. [ABSTRACT FROM AUTHOR]

Cityscapes contain a variety of objects, each with a particular role in urban administration and development. With the rapid growth and implementation of 3D imaging technology, urban areas are increasingly surveyed with high-resolution point clouds. This technical advancement extensively improves our ability to capture and analyse urban environments and their small objects. Deep learning algorithms for point cloud data have shown considerable capacity in 3D object classification but still face problems with generally under-represented objects (such as light poles or chimneys). This paper introduces the ESTATE dataset (https://github.com/3DOM-FBK/ESTATE), which combines available datasets of various sensors, densities, regions, and object types. It includes 13 classes featuring intensity and/or colour attributes. Tests using ESTATE demonstrate that the dataset improves the classification performance of deep learning techniques and could be a game-changer to advance in the 3D classification of urban objects. [ABSTRACT FROM AUTHOR]

Following the enormous technological developments of LiDAR (Light Detection And Ranging) sensors, it is currently easier to find them commercially in the UA (Uncrewed Aerial Systems) sector. In particular, with the Zenmuse L1 by DJI (Dà-Jiāng Innovations) the market has grown globally, mainly due to the compactness of the product that is easily compatible with UAS. The L1 sensor can record up to three returns of the emanating signal, so it can acquire a larger amount of points, such as those below the vegetation. Therefore, in addition to the geometric information of the points, the Zenmuse L1 point clouds also provide other information, such as the number of echo returns from 1 to 3. This data could be exploited to improve the automatic extraction of the digital terrain model (DTM) from the point clouds, hopefully leading to the avoidance of manual correction. This research aims to focus on evaluating whether the addition of the return number feature can affect the identification of the ground points through different computational methods and can improve the time efficiency of state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In recent years, power line inspections have benefited from the use of the lidar surveying technology, which enables safe and rapid data acquisition, even in challenging environments. To further optimize monitoring operations and reduce time and costs, automatic processing of the point clouds obtained is of greatest importance. This work presents a complete pipeline for processing power line data that includes (i) lidar point cloud segmentation using a Fully Convolutional Network, (ii) individual pylon identification via DBSCAN clustering, and (iii) the automatic extraction and modelling of any number of cables using a multi-model fitting algorithm based on the J-Linkage method. The proposed procedure is tested on a 36 km-long power line, resulting in a F1-score of 97.6% for pylons and 98.5% for the vectorized cables. [ABSTRACT FROM AUTHOR]

The processing of bathymetric LiDAR waveforms is an important task, as it provides range and radiometric information to determine the precise location of water surface and bottom, and other characteristics like amplitude. The exponential waveform decomposition proved to be an effective algorithm for bathymetric LiDAR waveforms processing, however, it heavily relies on the high-quality initial estimates of the model parameters. This paper proposes to make use of deep learning to obtain the initial values directly from the input received waveforms without any hand-crafted features and prior-knowledges. Additionally, to provide training samples, we presents a method to create the synthetic bathymetric LiDAR waveforms by simulating of the backscatter cross function returned from water bodies. Two networks with different sensitivities of weak signals were trained by these synthetic waveforms, and used to estimate the initial values of the model parameters, a least square optimization follows up to obtain the final waveform decomposition result. This deep learning assisted exponential waveform decomposition method is applied to the real waveforms acquired by RIEGL VQ-840-G. The results show that estimations with the help of deep learning is less influenced by the intermediate peaks backscattered from objects and particles in water, producing a cleaner point cloud with less isolated points below water surface than the original exponential waveform decomposition. Moreover, the proposed sensitive DL-XDC is even able to detect some very weak bottom returns with low SNR. [ABSTRACT FROM AUTHOR]

In the fields of photogrammetry, computer vision and computer graphics, the task of neural 3D scene reconstruction has led to the exploration of various techniques. Among these, 3D Gaussian Splatting stands out for its explicit representation of scenes using 3D Gaussians, making it appealing for tasks like 3D point cloud extraction and surface reconstruction. Motivated by its potential, we address the domain of 3D scene reconstruction, aiming to leverage the capabilities of the Microsoft HoloLens 2 for instant 3D Gaussian Splatting. We present HoloGS, a novel workflow utilizing HoloLens sensor data, which bypasses the need for pre-processing steps like Structure from Motion by instantly accessing the required input data i.e. the images, camera poses and the point cloud from depth sensing. We provide comprehensive investigations, including the training process and the rendering quality, assessed through the Peak Signal-to-Noise Ratio, and the geometric 3D accuracy of the densified point cloud from Gaussian centers, measured by Chamfer Distance. We evaluate our approach on two self-captured scenes: An outdoor scene of a cultural heritage statue and an indoor scene of a fine-structured plant. Our results show that the HoloLens data, including RGB images, corresponding camera poses, and depth sensing based point clouds to initialize the Gaussians, are suitable as input for 3D Gaussian Splatting. [ABSTRACT FROM AUTHOR]

Point clouds acquired by Mobile Mapping System (MMS) are useful for creating 3D maps that can be used for autonomous driving and infrastructure development. However, many applications require semantic labels to each point of the point clouds, and the manual labeling process is very time consuming and expensive. Therefore, there is a strong need to develop a method to automatically assigning semantic labels. For automatic labeling tasks, classification methods using multiscale features are effective because multiscale features include features of various scales of roadside objects. Multiscale features are calculated using points inside spheres of multiscale radii centered at each point in a point cloud. When calculating multiscale features that are useful for classifying MMS point clouds, it is necessary to calculate features using relatively large radii. However, when calculating multiscale features using wide range of neighbor points, existing methods, such as kd-tree, require unacceptably long computation time for neighbor search. In this paper, we propose a method to calculate multiscale features in practical time for semantic labeling of large-scale point clouds. In our method, an MMS point cloud is first divided into small spherical regions. Then, radius search using multiscale radii is performed, and multiscale features are calculated using those neighbor points. Our experimental results showed that our method achieved significantly faster computational performance than conventional methods, and multiscale features could be calculated from large-scale point clouds in practical time. [ABSTRACT FROM AUTHOR]

Corals are critical reef-building organisms, providing essential habitat and ecosystem services. Tracking coral growth over time indicates coral reef health, which can be measured using various established techniques. Several coral growth-related studies have successfully applied photogrammetry to a particular coral of various types. While the focus of previous work was on standardised data processing and, to a certain degree, on the assessment of different point cloud comparison methods (Lange et al. 2022), little attention has been given to the impact of camera calibration. This study measured the annual linear extension of five Acropora spp. colonies using photogrammetry and evaluated all stages of imagery processing. A high focus was given to the analysis of the camera calibration method and the validation of camera parameters derived using an in-situ calibration of coral images with scale bars placed in the camera's field of view. We demonstrate that this method is as reliable as the calibration using a calibration frame. This study also examined the impact of the different point cloud comparison methods for Acropora spp. More specifically, the derived point clouds are compared by applying the point-to-point and point-to-model methods and manually selecting 12 coral branch tips. Histograms derived from the comparison methods were analysed and deemed a suitable and efficient alternative approach for measuring the maximum growth rate of mature colonies over shorter time periods (1 year or less). [ABSTRACT FROM AUTHOR]

Recent advances have demonstrated that Language Vision Models (LVMs) surpass the existing State-of-the-Art (SOTA) in two-dimensional (2D) computer vision tasks, motivating attempts to apply LVMs to three-dimensional (3D) data. While LVMs are efficient and effective in addressing various downstream 2D vision tasks without training, they face significant challenges when it comes to point clouds, a representative format for representing 3D data. It is more difficult to extract features from 3D data and there are challenges due to large data sizes and the cost of the collection and labelling, resulting in a notably limited availability of datasets. Moreover, constructing LVMs for point clouds is even more challenging due to the requirements for large amounts of data and training time. To address these issues, our research aims to 1) apply the Grounded SAM through Spherical Projection to transfer 3D to 2D, and 2) experiment with synthetic data to evaluate its effectiveness in bridging the gap between synthetic and real-world data domains. Our approach exhibited high performance with an accuracy of 0.96, an IoU of 0.85, precision of 0.92, recall of 0.91, and an F1 score of 0.92, confirming its potential. However, challenges such as occlusion problems and pixel-level overlaps of multi-label points during spherical image generation remain to be addressed in future studies. [ABSTRACT FROM AUTHOR]

The interest in studies on 3D city models has renewed over time thanks to technological innovations that concern the methods of data acquisition and automatic classification of unstructured point clouds, to model increasingly specialized geometric objects for the growing needs of urban transformation management. Managing geometric and semantic information in cognitive systems, based on standards, increasingly open to the inquiries, analyses, and predictions that today are necessary to implement for future assessments, can digitally twin reality. The problem focused on in this work is the modelling phase, which requires efficient automatisms to make the production phase of geometric objects more sustainable, whether they are then destined to become parametric features in a BIM/CIM environment or 3D geometric objects of a geodatabase.A comprehensive methodology for automating the generation of 3D meshes from point cloud data using Python code, with a focus on practical implementation is presented parallelly to evaluations regarding the generation of the geometric 3D contents of the urban environment representation, depending on whether or not it includes examples of architectural heritage (of all ages). Leveraging the Open3D library and NumPy, the methodology provides different experiences, tests, and related discussions in the field of architectural and urban 3D surface reconstruction and visualization. The paper outlines essential steps including data loading and preparation, meshing strategy selection, data processing using Poisson reconstruction, and mesh exportation and visualization. Additionally, a method for generating multiple Levels of Detail (LoD) meshes is introduced, enhancing the applicability of the approach for city modelling framework applications, including architectural elements of buildings. The presented Python code serves as a practical resource for implementing 3D mesh generation workflows seamlessly. [ABSTRACT FROM AUTHOR]

The stability of front-end odometry output is the cornerstone of real-time mapping and high-precision localization. However, in autonomous driving, factors such as sensor types, driving speed, and signal occlusion can impact system performance, particularly causing trajectory drift, reduced accuracy, and decreased reliability during high-speed travel. This study employs a point-by-point update approach for odometry, achieving high-frequency odometry output and mapping updates nearly matching the point sampling rate, effectively eliminating point cloud distortions and providing solid support for tracking high-speed motion and navigation localization. This method emphasizes maintaining registration accuracy and robustness under high-frequency output conditions. Moreover, by utilizing backward propagation to optimize the estimation of posture and position, and ignoring inter-frame processing, this research implements an iterative extended Kalman filter, achieving tight coupling with IMU data. Additionally, the use of the iVox data structure accelerates point cloud searching, enhancing system processing efficiency. Testing in various scenarios has demonstrated that our method exhibits superior performance and robustness across different scene data, showcasing its potential application in the field of autonomous driving. [ABSTRACT FROM AUTHOR]

Urban three-dimensional modeling involves digitizing geographical elements such as urban ground and architecture, profoundly impacting urban management, planning, and development. In recent years, such models have demonstrated various advantages, but traditional challenges in acquiring comprehensive geographical information persist. The emergence of unmanned aerial vehicle (UAV) oblique photography offers a viable solution, enabling cost-effective acquisition of geographical information and facilitating the construction of three-dimensional urban models. However, UAV-based 3D reconstruction encounters certain issues.Conventional 3D reconstruction typically begins with data collection, involving two-dimensional images or point cloud data acquisition using imagery or LiDAR technology. Subsequent steps include data preprocessing, feature extraction and matching, surface model construction, texture mapping, rendering, and generating three-dimensional models. However, traditional methods exhibit limitations and drawbacks, such as inadequate adaptation to complex scenes, increased computational demands for large-scale scenes, and difficulties with dynamic scenes.Deep learning-based 3D reconstruction methods, like MVSNet, employ deep neural networks to infer scene depth information from multiple-view images, yielding high-quality 3D reconstruction results. However, they rely heavily on prior datasets and auxiliary information, limiting generalization.Neural Radiance Fields (NeRF) combine neural networks with volumetric rendering techniques, excelling in reconstructing objects with high precision and detail, handling dynamic scenes, and addressing areas with sparse viewpoints. However, NeRF's input requires sparse point clouds, commonly obtained using COLMAP, which has limitations. To address these limitations, using NeRF with global Structure-from-Motion (SfM) has emerged as a promising solution. This simultaneous processing of the entire dataset estimates camera trajectories and scene structure from shared features and information among multiple images, resulting in more accurate sparse point clouds and significantly improving image quality. Experimental validation using open-source and self-generated datasets demonstrates that this algorithm markedly enhances surface texture quality compared to traditional 3D reconstruction and NeRF with incremental SfM. In summary, this algorithm enhances 3D reconstruction efficacy and exhibits superior robustness, scalability, and accuracy compared to conventional methods. [ABSTRACT FROM AUTHOR]

This paper introduces an enhanced approach to loop closure detection in Simultaneous Localization and Mapping (SLAM) by integrating the Faster-LIO framework with Stable Triangle Descriptors (STD). SLAM, essential in autonomous driving and augmented reality, often encounters cumulative errors affecting mapping accuracy. Traditional detection methods based on sensor data like camera images and LiDAR point clouds struggle with environmental changes that alter scene appearance and geometry. Our approach utilizes LiDAR sensors and STD, exploiting the geometric stability of triangles to maintain robustness against rotational and translational changes. The process involves storing triangle descriptors from key frames in a hash table within the Faster-LIO framework, a voxel-based LiDAR-Inertial Odometry optimized for efficiency and speed. These descriptors are then matched across frames using a voting mechanism to ensure reliable loop closure detection. Validation on the KITTI dataset and a proprietary subterranean parking garage dataset demonstrates that this integration not only enhances loop closure detection but also simplifies computational demands by avoiding complex tree structures. This method shows promise for broader applications in robotics and autonomous systems, with future research focusing on refining the descriptor and expanding its applicability to other sensor modalities. [ABSTRACT FROM AUTHOR]

RGB-D sensors offer a low-cost and promising solution to streamline the generation of BIM models. This paper introduces a framework designed to automate the creation of detailed and semantically rich BIM models from RGB-D data in indoor environments. The framework leverages advanced computer vision and deep learning techniques to overcome the challenges associated with traditional, labour-intensive BIM modeling methods. The results show that the proposed method is robust and accurate, compared to the high-quality statistic laser scanning TLS. Indeed, 58% of the distances measured between the calculated and the reference point cloud produced by TLS were under 5 cm, and 82% of distances were smaller than 7 cm. Furthermore, the framework achieves 100% accuracy in element extraction. Beyond its accuracy, the proposed framework significantly enhances efficiency in both data acquisition and processing. In contrast to the time-consuming process associated with TLS, our approach remarkably reduces the data collection and processing time by factor of height. This highlights the framework's substantial improvements in accuracy and efficiency throughout the BIM generation workflows, making it a streamlined and time-effective solution. [ABSTRACT FROM AUTHOR]

3D point clouds acquired with terrestrial or mobile LiDAR sensors are increasingly used to map urban forests. The segmentation of separate tree instances, i.e., subsets of points representing individual trees, is a relevant step in automatically extracting tree inventory data from 3D point clouds. Various algorithms have been proposed for tree instance segmentation, offering different trade-offs between accuracy, runtime, and robustness against data incompleteness and noise. In this work, we propose a coarse-to-fine algorithm for segmenting tree instances in urban 3D point clouds from terrestrial or mobile LiDAR scanning that combines two existing techniques: (1) the computationally efficient marker-controlled Watershed algorithm and (2) a more accurate 3D region growing algorithm. Initially, the marker-controlled Watershed algorithm generates a coarse segmentation, which is further improved by a Voronoi segmentation-based error removal. Subsequently, the coarse segmentation is refined by the 3D region growing algorithm in areas with overlapping tree crowns and sufficient data quality. In both steps, our algorithm uses the results of a prior semantic segmentation to select suitable markers and seed points. We evaluated our coarse-to-fine algorithm in an ablation study using two mobile LiDAR datasets and one terrestrial LiDAR dataset from three German cities. Our results show that our algorithm outperforms the standard marker-controlled Watershed algorithm in terms of panoptic quality by 3.7, 25.5, and 29.6 percentage points, respectively, while being computationally more efficient than an approach purely based on 3D region growing. [ABSTRACT FROM AUTHOR]

3D point clouds are a relevant source of information for multiple applications, including digital twins, building modeling, disaster and risk management, forestry, autonomous driving, and many others. Assigning points to the semantic classes is one of the essential data interpretation steps to effectively use them for further analysis. Deep learning models for semantic segmentation, such as RandLA-Net, are state-of-the-art methods for this task. Although the overall accuracy of classification is usually satisfactory,there are still several shortcomings not allowing assigning correct labels across all the classes. For instance, the receptive field of these networks is often too small to correctly classify point clouds in all cases. These networks suffer also from class imbalance, typical in real-world data sets, and tend to oversmooth small classes. Post-processing approaches help to overcome these problems and achieve better classification accuracy. In this work, we investigate the feasibility of improving the deep-learning outputs by introducing prior knowledge. To do this, the output probabilities of point classes obtained using RandLA-Net are post-processed with a workflow based on Markov Random Fields, in which the unary potentials are adjusted to preserve smaller classes while the pairwise potentials take into account. a hand-tailored inter-class reliability matrix. To validate our method, we apply it to the Hessigheim benchmark. Our MRF-based approach further optimizes these prediction results, effectively and efficiently improving the overall accuracy by approximately 1 to 2 percentage points. [ABSTRACT FROM AUTHOR]

Thermal point clouds integrate thermal radiation and laser point clouds effectively. However, the semantic information for the interpretation of building thermal point clouds can hardly be precisely inferred. Transferring the semantics encapsulated in 3D building models at Level of Detail (LoD)3 has a potential to fill this gap. In this work, we propose a workflow enriching thermal point clouds with the geo-position and semantics of LoD3 building models, which utilizes features of both modalities: model point clouds are generated from LoD3 models, and thermal point clouds are co-registered by coarse-to-fine registration. The proposed method can automatically co-register the point clouds from different sources and enrich the thermal point cloud in facade-detailed semantics. The enriched thermal point cloud supports thermal analysis and can facilitate the development of currently scarce deep learning models operating directly on thermal point clouds. [ABSTRACT FROM AUTHOR]

In the realm of autonomous systems and smart-city initiatives, accurately detecting and localizing pole-like objects (PLOs) such as electrical poles and traffic signs has become crucial. Despite their significance, the diverse nature of PLOs complicates their accurate recognition. Point cloud data and 3D deep learning models offer a promising approach to PLO localization under varied lighting, addressing issues faced by camera systems. However, the distinct characteristics of different street scenes worldwide require infeasibly extensive training data for satisfactory results because of the nature of deep learning. This prohibitively increases the cost of lidar data capture and annotation. This paper introduces a novel few-shot learning framework for the classification of outdoor point cloud objects, leveraging a minimalistic approach that requires only a single support sample for effective classification. Central to our methodology is the development of Pole-NN, a Non-parametric Network that efficiently distinguishes between various PLOs and other road assets without the need for extensive training datasets traditionally associated with deep learning models. Additionally, we present the Parkville-3D Dataset, an annotated point cloud dataset we have captured and labelled, which addresses the notable scarcity of fine-grained PLO datasets. Our experimental results demonstrate the potential of our approach to utilize the intrinsic spatial relationships within point cloud data, promoting a more efficient and resource-conscious strategy for PLO classification. [ABSTRACT FROM AUTHOR]

Data organization is essential for effective analysis of the spatial relationships between rooms and walls. Segmentation in successive stages plays a crucial role in this process since dividing the data set into smaller sets makes its analysis easier. The proposed approach starts with the segmentation of buildings by storeys using a three-dimensional point cloud and is carried out by detecting peaks in histogram of Z frequency. Subsequently, each storey is segmented into rooms using three-dimensional mathematical morphology techniques, which allows the delimitation of the interior spaces. The third and final step consists of identifying elements within each room, such as doors, ceiling, floor, and walls. During this process, connectivity and adjacency of building elements are studied to automatically derive topological graphs. This methodology results in a deeper and more systematic analysis of three-dimensional spaces, providing a solid basis for the subsequent interpretation and manipulation of the data obtained. The proposed method has been tested in two real cases and the results are shown respectively. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for the analysis of urban road markings using point cloud data, aiming to enhance the understanding and functionality of urban spaces. Our method employs intensity-based analysis along with hierarchical clustering and object descriptors to identify and categorize road markings while maintaining the original point cloud format. Then, we conceptualize a new space distribution from segmenting urban spaces into distinct functional areas based on road markings. Our results indicate the potential of our approach in providing insights that could support urban planning, and autonomous vehicle navigation systems, yet further research and validation are essential to fully realize its potential. [ABSTRACT FROM AUTHOR]

With the rapid advancement of 3D sensing technologies, obtaining 3D shape information of objects has become increasingly convenient. Lidar technology, with its capability to accurately capture the 3D information of objects at long distances, has been widely applied in the collection of 3D data in urban scenes. However, the collected point cloud data often exhibit incompleteness due to factors such as occlusion, signal absorption, and specular reflection. This paper explores the application of point cloud completion technologies in processing these incomplete data and establishes a new real-world benchmark Building-PCC dataset, to evaluate the performance of existing deep learning methods in the task of urban building point cloud completion. Through a comprehensive evaluation of different methods, we analyze the key challenges faced in building point cloud completion, aiming to promote innovation in the field of 3D geoinformation applications. Our source code is available at https://github.com/ tudelft3d/Building-PCC-Building-Point-Cloud-Completion-Benchmarks.git. [ABSTRACT FROM AUTHOR]

Nowadays, semantic segmentation results of 3D point cloud have been widely applied in the fields of robotics, autonomous driving, and augmented reality etc. Thanks to the development of relevant deep learning models (such as PointNet), supervised training methods have become hotspot, in which two common limitations exists: inferior feature representation of 3D points and massive annotations. To improve 3D point feature, inspired by the idea of transformer, we employ a so-call LCP network that extracts better feature by investigating attentions between target 3D points and its corresponding local neighbors via local context propagation. Training transformer-based network needs amount of training samples, which itself is a labor-intensive, costly and error-prone work, therefore, this work proposes a weakly supervised framework, in particular, pseudo-labels are estimated based on the feature distances between unlabeled points and prototypes, which are calculated based on labeled data. The extensive experimental results show that, the proposed PL-LCP can yield considerable results (67.6% mIOU for indoor and 67.3% for outdoor) even if only using 1% real labels, and comparing to several state-of-the-art method using all labels, we achieve superior results in mIOU, OA for indoor (65.9%, 89.2%). [ABSTRACT FROM AUTHOR]

3D building models is crucial for applications in smart cities. Automatic reconstruction of 3D buildings has been investigated based on various data sources. Point clouds from airborne LiDAR scanners can be used to extract buildings data due to its high accuracy and point density. In this paper, we present a methodology to segment buildings and corresponding rooftop structure from point clouds. First, RandLA-Net, which is an efficient and lightweight neural network for semantic segmentation of large-scale point clouds, is revised and adopted for building segmentation. By implementing local feature aggregation of each point, RandLA-Net can effectively preserve geometric details in point clouds. Besides 3D coordinates of point clouds, we incorporated point attributes including pulse intensity and return numbers into the network as additional features. Feature normalizations are applied to the input features. To achieve a better result of the local feature aggregation, hyperparameters of the network are fine-tuned according to the density of points and building size. Based on the classified building point clouds, DBSCAN clustering algorithm is implemented for segmenting individual buildings. Elevation histogram analysis is conducted to determine optimal threshold values for delineating candidate rooftop point clouds of individual buildings. For the buildings with multiple rooftops, multiple elevation threshold values are necessary to extract corresponding rooftops or walls. Then DBSCAN is employed again for segmentation of individual rooftops and denoising of point clouds of each building. Finally, Alpha-shape analysis is applied based on adaptive threshold values to build the envelope of each rooftop. Experiments show that our implementation of building segmentation using RandLA-net achieves higher mean IoU (Intersection over Union) and better classification performance in building segmentation. ISPRS benchmark data was used in our experiment and our methodology produce results with accuracy of 90.79%. [ABSTRACT FROM AUTHOR]

HD Maps (High-Definition Maps) serve as crucial resources for the domain of autonomous vehicle. Because HD Maps can provide detailed and accurate road information, the generation of HD Maps has been a labour-intensive and high cost. This research presents an innovative and semi-automated approach for efficient HD Maps generation by using assure mapping tool with deep learning techniques and mobile laser scanned point cloud geometry. The proposed method starts with data collection from various sources such as images, LiDAR point clouds, and integrated INS/GNSS trajectory data. These data are labelled by using a pre-trained model. After finishing post-labelling, these data are subjected to deep learning training by using VoxelNet and Yolact++ framework and leading to the generation of an AI model. The tool effectively recognizes and categorizes features such as road surface markings, traffic signs, and traffic lights, which can be further expanded as per requirements. Finally, the output format can be converted to OpenDRIVE, Lanelet2, and other else. Hence, the extracted lane lines can compare to the manual mapping data for verifying the accuracy. This study demonstrates that the proposed approach can be instrumental in streamlining the HD Maps generation procedure, reducing manual labour, and enhancing efficiency. The assure mapping tool proves to be an effective instrument, particularly when powered by deep learning algorithms and point cloud geometries, in the creation of reliable, comprehensive, and application-ready HD Maps. [ABSTRACT FROM AUTHOR]

The technology for 3D reconstruction of tree models based on point clouds has been extensively researched, necessitating effective datasets for the study of branch and leaf separation, skeleton point extraction, and tree parameter extraction methods. However, existing datasets for 3D tree models face several challenges, including insufficient data volume for deep learning network training, low accuracy of model ground truth impeding effective method precision evaluation, and a lack of dataset richness to satisfy the needs of multi-type method assessments. In response to these challenges, This paper introduces, for the first time, a fully automated method for generating structured three-dimensional synthetic tree models, and constructs a large-scale 3D synthetic tree dataset enriched with comprehensive structural information. This method facilitates automated computation across several processes, including the mass generation of simulated trees, separation of branches and leaves, noise generation, extraction of skeleton points, and volume calculation. To validate the usability of this dataset across various applications, this paper employs state-of-the-art (SoTA) algorithms to verify the accuracy of methods in 3D tree model reconstruction and carbon stock calculation, thereby thoroughly demonstrating the dataset's effectiveness. [ABSTRACT FROM AUTHOR]

To solve the current problem of insufficient exploration of three-dimensional spatial information detected by 3D ground penetrating radar (3D GPR) and the data processing mainly based on the analysis and interpretation of two-dimensional slice images, a method is proposed to extract underground target based on the local entropy feature of discrete point clouds after the voxelization of 3D GPR data. First, the acquired 3D GPR data was voxelized into discrete three-dimensional point clouds. Then the local entropy feature of the voxelized point clouds over the entire region were calculated. The soil background and underground targets were distinguished by classifying them from multiple dimensions through Support Vector Machine (SVM). Finally, the urban road underground environment was taken as the research object, and this method was used for experimental analysis using measured data. The experimental results show that the accuracy of this method in extracting underground targets is as high as 90.1%, and the missing detection rate of missing underground targets is as low as 7.8%. The proposed method is accurate and effective, providing a new approach for 3D GPR to extract underground target. [ABSTRACT FROM AUTHOR]

Data capture in UAV photogrammetry is carried out using two main methodologies: frame frame-based and video video-based. Frame Frame-based data gathering is the preferred method among UAV projects because to its inherent reliability in calibration. Nonetheless, circumstances involving moving objects or occlusions inside the measured region may produce unsatisfactory results utilizing this method. I In response to these challenges, video video-based data collecting appears as a potential option, capable of creating a series of successive images that together alleviate the constraints outlined above. In this study we aim to compare the usefulness of frame and video images in building 3D models, using both oblique and vertical image orientations. Rigorous evaluations produced many outputs, including dense point clouds, digital surface models (DSM), meshes, and orthophotographs. The evaluation criteria included da ta acquisition velocity, processing efficiency, calibration precision, distortion analysis, residual plots, scale correctness, and reprojection error. The empirical results demonstrated the advantages of video video-frame captures in improving the quality of the resulting 3D models. Notably, the use of video frames resulted in a significant reduction in reprojection error by 16%, calibration residuals by 36%, distortions by up to 51%, and processing time by 27%. Thus, it seems that integrating video frames improves data gathering accuracy and speeds up processing, replacing standard frame images with video counterparts for increased efficacy. [ABSTRACT FROM AUTHOR]

Enhancing the autonomous landing capability of unmanned aerial vehicles (UAVs) is of great significance for improving their operational efficiency and field survival capabilities. To this end, we propose a real-time autonomous landing spot detection method for UAVs. Firstly, the pose of the UAV at any given time and the initial three-dimensional point cloud of the scene are estimated using simultaneous localization and mapping (SLAM) techniques. Then, since the initial point cloud is sparse and cannot be used for terrain analysis, we generate a voxel-based elevation map of the scene, which can establish interconnectivity among the points. Finally, we propose a shift-box strategy to comprehensively analyze various terrain factors in the elevation map, determine the landing spot for UAVs, and update the landing spot in real time. The UAV flight experiments conducted in the real world have demonstrated the effectiveness and real-time performance of the proposed method. [ABSTRACT FROM AUTHOR]

The extraction of roadside lampposts constitutes a significant research focus within the domain of vehicular LiDAR point cloud object retrieval. Addressing the complexities inherent in discerning lampposts amidst convoluted vegetation in diverse roadway settings, this study introduces an innovative, progressive technique for the individualized extraction of lampposts utilizing vehicular LiDAR point clouds. The proposed method initiates with a bifurcation of the primary point cloud into terrestrial and aerial subsets via the Cloth Simulation Filter (CSF) algorithm. Subsequent processes involve the extraction of distinct lamppost structures from aerial point clouds through a methodology integrating elevation-normalized spatial partitioning and directional coverage analysis, thereby facilitating precise lamppost localization. The culmination of this process involves the refinement of lamppost point clouds through supervoxel clustering complemented by a non-discretization filter grounded in Principal Component Analysis (PCA). The efficacy of this novel approach is substantiated through empirical studies employing a LiDAR dataset encompassing extensive adhesive scenarios, whereupon comparative analysis with extant methodologies reveals its enhanced proficiency in isolating individual lampposts in complex environments. [ABSTRACT FROM AUTHOR]

Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete point clouds of obstructed objects and capturing indoor scene details. Therefore, this research introduces an innovative and effective reconstruction framework based on deep learning semantic segmentation and model-driven techniques to address these limitations. The proposed framework utilizes wall segment recognition, feature extraction, opening detection, and automatic modeling to reconstruct 3D structured models of point clouds with different room layouts in both Manhattan and non-Manhattan architectures. Moreover, it provides 3D BIM models of actual unstructured elements by detecting objects, completing point clouds, establishing bounding boxes, determining type and orientation, and automatically generating 3D BIM models with a parametric algorithm implemented into the Revit software. We evaluated this framework using publicly available and locally generated point cloud datasets with varying furniture combinations and layout complexity. The results demonstrate the proposed framework's efficiency in reconstructing structured indoor elements, exhibiting completeness and geometric accuracy, and achieving precision and recall values greater than 98%. Furthermore, the generated unstructured 3D BIM models keep essential real-scene characteristics such as geometry, spatial locations, numerical aspects, various shapes, and orientations compared to literature methods. [ABSTRACT FROM AUTHOR]

When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple's iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LIDAR) is widely acknowledged as a robust tool for monitoring forest structure, dynamics, and changes. To achieve a high-complete forest structural model, LiDAR data acquisition from both aerial (above-canopy) and terrestrial (below-canopy) platforms is commonplace. Consequently, in such multi-modal LiDAR cases, robust data registration is required for accurate forest analysis, such as biomass and canopy growth. Yet, multi-modal LiDAR registration remains a significant challenge due to differences in observation perspectives, deficient data overlap, and often inhomogeneity in point distributions and densities. The challenge increases in complex forest environments due to the abundance of unstable features (e.g., leaves) and occlusions. Thus, the dynamic nature of forest scenes needs to be considered when applying registration methods on forest point clouds. In this paper, we overview the latest advancements in registering forest point clouds from multi-modal data acquisitions, aiming to discuss the strengths and weaknesses of the most used LiDAR registration methods for forest applications. To support our investigations, we benchmark two multi-modal registration methods especially designed for forest mapping against traditional global and feature-based approaches. Experiment assessments were conducted using two point clouds acquired from a permanent laser scanning and airborne laser scanning systems at a boreal forest plot. [ABSTRACT FROM AUTHOR]

This paper presents a systematic review of published case studies on Heritage Building Information Modelling (HBIM) since 2018, and identifies research gaps in the subject matter. Building upon the foundational work of Ewart and Zuecco (2019), this research aims to reveal the latest trends in HBIM implementation, identify recent developments of HBIM technologies, changes in the purpose of HBIM programs and stakeholder roles and responsibilities, and uncover knowledge gaps that provide avenues for future research. Utilizing the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach, two major academic databases, Scopus and Web of Science (WOS), were searched, resulting in a rich and diverse dataset for analysis. The paper reports findings on the status of reality capture techniques used to acquire data for HBIM development, focusing on terrestrial laser scanning (TLS) technology. The review highlights the benefits and limitations of TLS for data acquisition in HBIM, as well as the integration of TLS with other reality capture technologies, such as Structure from Motion (SfM) and photogrammetry. The paper further outlines the typical workflow for processing TLS scan data and explores the integration of multiple point clouds for comprehensive heritage site modeling. In addition to the state of the art, this systematic review also uncovers several research gaps in the field of HBIM that offer opportunities for future research and innovation, including the lack of guidelines for data acquisition in HBIM programs, the predominantly manual development process of HBIM from TLS point cloud data, and the under-utilized capacity of TLS for long-term monitoring and change detection. This comprehensive review provides valuable insights into the current landscape of HBIM, offering guidance for future research and development in the heritage sector and highlighting areas in need of further investigation to advance the field. [ABSTRACT FROM AUTHOR]