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Structure from Motion (SfM) is a computer vision technique used to reconstruct three-dimensional (3D) structures from a series of two-dimensional (2D) images or video frames. However, SfM tools struggle with transparent objects, reflective surfaces, and low-resolution frames. In such situations, image-based interactive 3D modeling software packages are employed to model 3D objects and measure dimensions. Our contributions to this work are twofold. First, we have introduced new tools to improve 3D modeling software packages; such tools are aimed at easing the workload for users. Second, we have conducted a comprehensive user study to evaluate the efficacy of popular 3d modeling software packages. The task is to measure certain dimensions for which ground truth measurements are already known. A relative error is calculated for every measurement. The evaluation of each software tool is done through survey form, event logs, and measurement relative error. The results of this user study clearly show that our approach to 3D modeling using multiple images has a lower relative error and produces higher quality 3D models than other software packages. In addition, it shows our new tools reduce the required time for completing a task.

Efficiency and efficacy are desirable properties for any evaluation metric having to do with Standard Dynamic Range (SDR) imaging or with High Dynamic Range (HDR) imaging. However, it is a daunting task to satisfy both properties simultaneously. On the one side, existing evaluation metrics like HDR-VDP 2.2 can accurately mimic the Human Visual System (HVS), but this typically comes at a very high computational cost. On the other side, computationally cheaper alternatives (e.g., PSNR, MSE, etc.) fail to capture many crucial aspects of the HVS. In this work, we present NoR-VDPNet++, a deep learning architecture for converting full-reference accurate metrics into no-reference metrics thus reducing the computational burden. We show NoR-VDPNet++ can be successfully employed in different application scenarios.

Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts.

We present MoReLab, a tool for user-assisted 3D reconstruction. This reconstruction requires an understanding of the shapes of the desired objects. Our experiments demonstrate that existing Structure from Motion (SfM) software packages fail to estimate accurate 3D models in low-quality videos due to several issues such as low resolution, featureless surfaces, low lighting, etc. In such scenarios, which are common for industrial utility companies, user assistance becomes necessary to create reliable 3D models. In our system, the user first needs to add features and correspondences manually on multiple video frames. Then, classic camera calibration and bundle adjustment are applied. At this point, MoReLab provides several primitive shape tools such as rectangles, cylinders, curved cylinders, etc., to model different parts of the scene and export 3D meshes. These shapes are essential for modeling industrial equipment whose videos are typically captured by utility companies with old video cameras (low resolution, compression artifacts, etc.) and in disadvantageous lighting conditions (low lighting, torchlight attached to the video camera, etc.). We evaluate our tool on real industrial case scenarios and compare it against existing approaches. Visual comparisons and quantitative results show that MoReLab achieves superior results with regard to other user-interactive 3D modeling tools.

Marine animal forests are benthic communities dominated by sessile suspension feeders (such as sponges, corals, and bivalves) able to generate three-dimensional (3D) frameworks with high structural complexity. The biodiversity and functioning of marine animal forests are strictly related to their 3D complexity. The present paper aims at providing new perspectives in underwater optical surveys. Starting from the current gaps in data collection and analysis that critically limit the study and conservation of marine animal forests, we discuss the main technological and methodological needs for the investigation of their 3D structural complexity at different spatial and temporal scales. Despite recent technological advances, it seems that several issues in data acquisition and processing need to be solved, to properly map the different benthic habitats in which marine animal forests are present, their health status and to measure structural complexity. Proper precision and accuracy should be chosen and assured in relation to the biological and ecological processes investigated. Besides, standardized methods and protocols are strictly necessary to meet the FAIR (findability, accessibility, interoperability, and reusability) data principles for the stewardship of habitat mapping and biodiversity, biomass, and growth data.

Shells made of structural glass are beautiful objects from both the aesthetics and the engineering point of view. However, they pose two significant challenges. The first one is to assure adequate safety and redundancy concerning possible global collapse. Being single-layered, in a shell made of structural glass, the brittle cracking of a single pane can lead to a sudden propagation of failure, up to instability. The second one is to guarantee cheap replacing possibilities for potentially collapsed components. This research explores a novel concept to address both requirements, where glass is both post-tensioned and reinforced and develops the research on TVT post-tensioned glass beams. Following the Fail-Safe Design (FSD) principles, a steel reinforcement relieves glass deficiencies (i.e. brittleness and low tensile strength). Following the Damage Avoidance Design (DAD) principles, glass segmentation and post-tensioning avoid the propagation of cracks. Up to now, glass-steel systems were limited to mono-dimensional elements (such as beams and columns) or simple bi-dimensional elements (arches, domes, barrel vaults). Instead, massive structures are usually realized as grid shells, where glass is used as simple cladding. This research investigates piecewise triangulated glass shells to enable the creation of 3D free-form glass-steel systems, where glass is load-bearing material. Hence, laminated glass panels are mechanically coupled with a filigree steel truss, whose elements are placed at the edges of the panel and act as an unbonded reinforcement. In a performance-based perspective, these steel trusses can be sized to bear at least the weight of all panels in the occurrence of simultaneous cracks (worst-case scenario). The panels are post-tensioned using a set of edge-aligned cables that add beneficial compressive stress on glass to prevent crack initiation. The cable placement and accompanying pre-loads are derived with an optimization strategy that minimizes the tensile stress acting on the shell. This optimization procedure also considers the practical constraints involved in the process. The results obtained through this automated procedure are later investigated using nonlinear FE analyses. The resulting structures optimize the total material usage providing contemporarily both transparency and load-bearing capabilities. Post-tensioned shells excel in static performances, achieving high stiffness and good redundancy for the worst-case scenario, and improve the structural lightness and the visual impact with respect to state-of-the-art competitors.

Virtual reality (VR) technologies have become more and more affordable and popular in the last five years thanks to hardware and software advancements. A critical issue for these technologies is finding paradigms that allow user interactions in ways that are as similar as possible to the real world, bringing physicality into the experience. Current literature has shown, with different experiments, that the mapping of real objects in virtual reality alongside haptic feedback significantly increases the realism of the experience and user engagement, leading to augmented virtuality. In this paper, we present a system to improve engagement in a VR experience using inexpensive, physical, and sensorized copies of real artefacts made with cheap 3D fabrication technologies. Based on a combination of hardware and software components, the proposed system gives the user the possibility to interact with the physical replica in the virtual environment and to see the appearance of the original cultural heritage artefact. In this way, we overcome one of the main limitations of mainstream 3D fabrication technologies: a faithful appearance reproduction. Using a consumer device for the real-time hand tracking and a custom electronic controller for the capacitive touch sensing, the system permits the creation of augmented experiences where the user with their hands can change the virtual appearance of the real replica object using a set of personalization actions selectable from a physical 3D-printed palette.

The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automatized by modern CNNs. This paper extends our previous work presented at the 3DUW’19 conference, outlining the workflow for the automated annotation of imagery from the first step of dataset preparation, to the last step of prediction reassembly. In particular, we propose an ecologically inspired strategy for an efficient dataset partition, an over-sampling methodology targeted on ortho-imagery, and a score fusion strategy. We also investigate the use of different loss functions in the optimization of a Deeplab V3+ model, to mitigate the class-imbalance problem and improve prediction accuracy on coral instance boundaries. The experimental results demonstrate the effectiveness of the ecologically inspired split in improving model performance, and quantify the advantages and limitations of the proposed over-sampling strategy. The extensive comparison of the loss functions gives numerous insights on the segmentation task; the Focal Tversky, typically used in the context of medical imaging (but not in remote sensing), results in the most convenient choice. By improving the accuracy of automated ortho image processing, the results presented here promise to meet the fundamental challenge of increasing the spatial and temporal scale of coral reef research, allowing researchers greater predictive ability to better manage coral reef resilience in the context of a changing environment.