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Low-light images often suffer from severe visual degradation, affecting both human perception and high-level computer vision tasks. Most existing methods process images in the spatial domain, making it challenging to simultaneously improve brightness while suppressing noise. In this paper, we present a novel perspective to enhance images based on frequency domain characteristics. Specifically, we reveal that the low-frequency components are closely related to luminance and color, whereas the high-frequency components are not. Based on this observation, we propose the Frequency-aware Network (FaNet) for low-light image enhancement. By selectively adjusting low-frequency components, FaNet preserves more high-frequency details while achieving low-light image enhancement. Additionally, we employ a multi-scale framework and selective fusion for effective feature learning and image reconstruction. Experimental results demonstrate the superiority of the proposed method. [Display omitted] • We reveal that the luminance is closely related to low-frequency components. • We design a frequency-aware network to utilize frequency domain features. • A multi-scale framework and selective fusion is proposed for feature learning. [ABSTRACT FROM AUTHOR]

For many applications, remote guidance and telerobotics provide great advantages. For example, tele-ultrasound can bring much-needed expert healthcare to isolated communities. However, existing tele-guidance methods have serious limitations including either low precision for video conference-based systems, or high complexity and cost for telerobotics. A new concept called human teleoperation leverages mixed reality, haptics, and high-speed communication to provide tele-guidance that gives an expert nearly-direct remote control without requiring a robot. This paper provides an overview of the human teleoperation concept and its application to tele-ultrasound. The concept and its impact are discussed. A new approach to remote streaming and control of point-of-care ultrasound systems independent of their manufacturer is described, as is a high-speed communication system for the HoloLens 2 that is compatible with ResearchMode API sensor stream access. Details of these systems are shown in supplementary video demonstrations. Novel interaction methods enabled by HoloLens 2-based pose tracking are also introduced and tests of the communication and user interaction are presented. The results show continued improvement of the system compared to previous work in instrumentation, HCI, and communication. The system thus has good potential for tele-ultrasound, as well as possible other applications of human teleoperation including remote maintenance, inspection, and training. The remote ultrasound streaming and control application is made available open source. [Display omitted] • System improvements to human teleoperation demonstrate its feasibility. • Other devices such as the Nreal Light can be used for implementation. • New device-agnostic remote ultrasound streaming and control demonstrated. • HoloLens pose tracking enables human teleoperation with limited compute resources. • HoloLens-based communication system provides effective sensor data streaming. [ABSTRACT FROM AUTHOR]

Point cloud completion aims to estimate the whole shapes of objects from their partial scans, and one of the main obstacles that prevents current methods from being applied in real-world scenarios is the variety of structural losses in real-scanned objects, which can hardly be fully included and reflected by the training samples. In this paper, we introduce Patch Sequence Autoregression Network (PSA-Net), a learning-based method that can be trained without the partial point clouds in dataset and is inherently adaptable to input scans with different levels of shape incompleteness: It makes restoring the unseen parts of objects be equivalent to predicting the missing tokens in local patch embedding sequences, and such prediction can start from any initial states. Specifically, we first introduce a Sequential Patch AutoEncoder that reconstructs complete point clouds from quantized patch feature sequences. Second, we establish a Mixed Patch Autoregression pipeline that can flexibly infer the whole sequence from any number of known tokens at any positions. Third, we propose a Voting-Based Mapping module that makes input points softly vote for their possible related tokens in sequences based on their local areas, which transforms partial point clouds to masked sequences in test. Quantitative and qualitative evaluations on two synthetic and four real-world datasets illustrate the competitive performances of our network when comparing with existing approaches. [Display omitted] • A Sequential Patch AutoEncoder for shape generation from quantized feature sequence. • A Mixed Patch Autoregression pipeline for token prediction from any initial states. • A Voting-based Mapping module for transformation from partial shapes to sequences. • Competitive performances on two synthetic and four real-world datasets. [ABSTRACT FROM AUTHOR]

Growing public interest in understanding the origins and production methods of dairy products, driven by concerns related to environmental impact, local sourcing, and ethics, highlights an important trend. Nevertheless, a knowledge-trust gap persists between consumers and the dairy industry. Addressing this gap, in this paper, we developed an immersive virtual farm simulation to provide realistic on-farm experiences to the public. Within the virtual farm, users can explore various sites where dairy cows are raised and gain insights into dairy production processes using a head-mounted display (HMD). This simulation was demonstrated at local libraries, involving 48 public participants. We collected and analyzed participants' feedback on various aspects, including usability and their overall perceptions, to assess the simulation's effectiveness as an agricultural education tool. We investigated the impact of the virtual experience on participants' perceived knowledge gain and their awareness of the dairy industry. The results indicate that our dairy farm simulation was positively received as an effective tool for public education. Emphasizing the potential of virtual reality (VR) simulations in agricultural education and the industry, we discuss our key findings and future plans. [Display omitted] • Development process to build a realistic immersive simulation of a dairy farm for public education purposes. • Findings on the usability and user perception of the immersive experience for education purposes. • The system is an effective and useful tool for learning and the provision of information about the dairy industry. [ABSTRACT FROM AUTHOR]

Typical non-local edge-aware filtering methods build long-range connections by deriving a minimum spanning tree (MST) from the input image. Each pixel on the MST only connects to a sub-set of pixels in the 8-connected neighborhood, resulting in piece-wise constant output with fake edges among sub-trees for the unbalanced information propagation along eight directions. In this paper, we propose two complementary spatial trees to incorporate information from the entire image. The structure of each tree depends on the spatial relationships of neighboring pixels. The distances between any two pixels in both spatial space and intensity space are the shortest distances on each tree. We introduce an efficient algorithm to recursively compute the output and the normalization constant on each tree with linear time complexity. For each pixel, we first calculate the outputs from eight subtrees and then fuse them to obtain the result on each tree structure. The final filtering output of our method is the weighted average of the results from two complementary spatial trees. Moreover, we present a distance mapping scheme to adjust the intensity distance between neighboring pixels, enabling our method to filter out a manageable degree of low-amplitude structures while sharpening major edges. Extensive experiments in graphic applications, such as image denoising, JPEG artifact removal, tone mapping, detail enhancement, and colorization, demonstrate the effectiveness and versatility of our method. [Display omitted] • Novel complementary trees to estimate the geodesic distance between any two pixels. • Efficient algorithms with linear time complexity to compute the weighted average of all pixels in the input image. • A distance mapping scheme in intensity space to manageably filter out low-amplitude structures. • Quantitative evaluation and qualitative comparison on various graphic applications to show the effectiveness and the versatility of our approach. [ABSTRACT FROM AUTHOR]

Image animation creates visually compelling effects by animating still source images according to driving videos. Recent work performs animation on arbitrary objects using unsupervised methods and can relatively robustly perform motion transfer on human bodies. However, the complex representation of motion and unknown correspondence between human bodies often lead to issues such as distorted limbs and missing semantics, which make human animation challenging. In this paper, we propose a semantically guided, unsupervised method of motion transfer, which uses semantic information to model motion and appearance. Specifically, we use a pre-trained human parsing network to encode the rich and diverse foreground semantic information, thus generating fine details. Secondly, we use a cross-modal attention layer to learn the semantic region's correspondence between human bodies to guide the network in selecting appropriate input features, prompting the network to generate accurate results. Experiments demonstrate that our method outperforms state-of-the-art methods in motion-related metrics, while effectively addressing the problems of semantic missing and unclear limb structures prevalent in human motion transfer. These improvements can facilitate its applications in various fields, such as education and entertainment. [Display omitted] • Proposes a novel framework for human image animation using semantic features and cross-modal attention. • Introduces semantic segmentation features to represent motion and identity information. • Employs a cross-modal attention mechanism to establish correspondences between semantic regions. • Achieves state-of-the-art performance on complex human motions like TaiChi poses. • Reduces issues of missing semantics and distorted limbs common in human animation. [ABSTRACT FROM AUTHOR]

This paper proposes a novel curve called the α - curve , which is specified by a variable shape parameter α and can be applied in curve reconstruction involving curve fairing, fitting, and segmentation. In contrast to classical log-aesthetic curves in which α is constant, this study contributes a novel formulation of α that varies monotonically within a specified segment. By introducing the variable α , the logarithmic curvature graph (LCG) becomes piecewise linear and parabolic, or any other function if necessary. Consequently, in comparison to conventional log-aesthetic curves that have limited field usage based on their rigidity, α -curves have various LCGs and flexible representations, thereby opening up many practical applications. Experimental results and comparative studies on curves created by CAD experts in the automotive industry demonstrate the theoretical and practical validity of α -curves. [Display omitted] • α -Curves are proposed by extending the mathematical framework of log-aesthetic curves • An α -curve is specified by a variable shape parameter • α -Curves have various log-curvature graphs (LCG) and flexible representations • α -Curves are applied to a new fairing, avoiding conventional issues of side effects [ABSTRACT FROM AUTHOR]

Learning 3D mesh representation is necessary for many computer vision and graphic tasks. Recently, some works have studied convolution methods for directly processing input meshes. However, these methods are usually weak in extracting local geometry information because of the disadvantages such as isotropic filter, neglect of mesh topology, and a small convolution field. In this paper, we introduce a local geometry-perceptive mesh convolution, which pays attention to mesh irregular structures for efficiently capturing geometry features in a multi-ring receptive field. Specifically, we define each template node's dynamic neighbor-attention weights used in multiple attention aggregation operations for obtaining local mesh change information of different vertices in the multi-ring field. After each aggregation, a shared anisotropic filter maps the catenation of each new vertex and its neighbors for extracting geometry features of the current ring. Then, complete local geometry features of each vertex in its large local field are obtained by summing the mapped results of each aggregation. Moreover, the position features of each vertex are added to its local geometry features to get the final representation vector of the vertex. We demonstrate the proposed mesh convolution method's strong ability in modeling 3D mesh shapes. [Display omitted] • Utilizing local irregular mesh structures, dynamic neighbor-attention weights of template's nodes perceive local mesh change information of different vertices. • Multiple attention aggregations and shared anisotropic filters help extract geometry features of large local receptive fields with multi-ring. • The proposed method determines our deep 3D morphable models (3DMMs) have smaller sizes than previous models and achieve state-of-the-art performances. [ABSTRACT FROM AUTHOR]

3D meshes are commonly utilized and may be considered to be the most popular surface representation in computer graphics due to their simplicity, efficiency and flexibility. However, the explicit storage of mesh vertices and connectivity, as in widely-used PLY and OBJ file formats, leads to substantial memory consumption. This, in turn, directly affects the processing and transmission in downstream applications. Though mesh simplification and mesh compression are common strategies to lessen memory consumption, they exhibit inherent limitations either in maintaining a balance between accuracy, efficiency, memory usage and mesh quality, or breaking the simplicity of explicit storage and struggling with optimizing the trade-off between compression performance and computational resource consumption. To overcome these limitations, inspired by the Gaussian Mixture Model (GMM), this paper proposes a GMM vertex-optimization-based implicitly-connected quadrilateral format for 3D mesh storage, named GMM-ICQ. Extensive qualitative and quantitative evaluations demonstrate that the GMM-ICQ format achieves efficient compression by retaining only a small amount of vertex information, while preserving sharp features and maintaining relatively high mesh quality. It also exhibits a certain degree of robustness in the presence of noise interference. Furthermore, benefiting from the inherent grid-based connectivity, the GMM-ICQ format maintains the simplicity of explicit storage and can be implemented as a progressive variant without incurring additional computational overhead. [Display omitted] • We present a GMM vertex-optimization-based implicitly-connected quadrilateral format for 3D mesh storage. • Simultaneously balances accuracy, efficiency, memory usage, and mesh quality. • Preserves the simplicity of explicit storage (such as PLY and OBJ). • No additional computational overhead needed for progressive variant implementation. [ABSTRACT FROM AUTHOR]

In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images. [Display omitted] • Structural reparameterization dehazenet: outstanding performance, faster speed. • Replacing Tanh with ReLU leads to better results. • Transfer learning addresses the problem of insufficient samples. • Dual subnets method proves highly effective in datasets of different scales. [ABSTRACT FROM AUTHOR]

Currently, deep learning-based 3D face reconstruction methods have shown promising results. However, they ignore the contextual information of the face, which is a topologically unified entirety. This paper proposes a 3D face reconstruction approach based on hybrid-level contextual information. Firstly, we suggest a regression network with contextual modeling capability at the feature level, PPR-CNet, which adopts a preferential parameter regression to regress the 3DMM parameters dynamically based on their various impacts on the reconstructed 3D face. Furthermore, we design a contextual landmark loss to constrain the face geometry at the landmark level. We introduce a differentiable renderer combined with multiple loss functions for weakly-supervised training. Quantitative experiments on two benchmarks show our method outperforms several SOTA methods. Extensive qualitative experiments indicate that our method performs efficiently in realism, facial proportion, and occlusion. [Display omitted] • We propose an approach to reconstruct a 3D face from a single image based on hybrid-level contextual information. • We propose a regression network, PPR-CNet, with contextual modeling capability, which regresses 3DMM parameters dynamically. • We design a contextual landmark loss to constrain face geometry employing landmark contextual information. [ABSTRACT FROM AUTHOR]

Low-light image enhancement is crucial in applications such as traffic safety and medical imaging. Besides having characteristics like low luminance and poor visibility, low-light images are inevitably affected by noise. Noise not only covers image details, introduces artifacts, and decreases image quality, but also interferes with downstream computer vision tasks like object detection, image segmentation, and object tracking. Previous methods in image enhancement often overlook noise handling or fail to accurately suppress noise in adaptive denoising processes, resulting in more severe noise artifacts in the enhanced images. To effectively suppress noise, this paper proposes a Channel Self-Attention Based Low-Light Image Enhancement Network (CAENet), which leverages Transformers and CNNs to model long-range and short-range pixel dependencies, extract global and local features, and construct a Noise Suppression Transformer Block that adaptively suppresses noise regions guided by signal-to-noise ratio priors and attention maps. After adaptive noise suppression, the resulting images exhibit fewer noise artifacts and improved details. The experimental results show that the network in this paper outperforms other state-of-the-art methods overall on five representative paired datasets as well as six unpaired datasets, improving the image quality while effectively suppressing the noise. [Display omitted] • To address the problem of diverse noise types and complex interference levels in low-light image enhancement, we propose an end-to-end network CAENet. • Since the severity of noise varies in regions with different luminance, we designed the NSTB based on channel self-attention. • Aiming at the different degree of interference of noise to the whole and details of the image in different sized regions, we design global and local branches. [ABSTRACT FROM AUTHOR]

Motion matching has become a widely adopted technique for generating high-quality interactive animation systems in video games. However, its current implementations suffer from significant computational and memory resource overheads, limiting its scalability in the context of modern video game performance profiles. Our method significantly reduces the computational complexity of approaches to motion synthesis, such as "Learned Motion Matching", while simultaneously improving the compactness of the data that can be stored and the robustness of pose output. As a result, our method enables the efficient execution of motion matching that significantly outperforms other implementations, by 8.5× times in CPU execution cost and at 80% of the memory requirements of "Learned Motion Matching", on contemporary video game hardware, thereby enhancing its practical applicability and scalability in the gaming industry and unlocking the ability to apply on large numbers of animated in-game characters. In this paper, we expand upon our published paper "Learning Robust and Scalable Motion with Lipschitz Continuity and Sparse Mixture of Experts", where we successfully proposed a novel method for learning motion matching that combines a Sparse Mixture of Experts model architecture and a Lipschitz-continuous latent space for representation of poses. We present further details on our method, with extensions to our approach to expert utilization within our neural networks. [Display omitted] • Applies the Sparse Mixture of Experts architecture to animation synthesis for the first time. • Significantly reduce theoretical and practical compute requirements, 8.5 times that of prior art. • Lipschitz-continuous encoding neural networks producing a linearly interpolatable manifold. • SMoE Networks express a higher density of animation data than Feed-Forward Neural Networks. [ABSTRACT FROM AUTHOR]

In this paper, we present a new volumetric ambient occlusion algorithm called Contextual Ambient Occlusion (CAO) that supports real-time clipping. The algorithm produces ambient occlusion images of exactly the same quality as Local Ambient Occlusion (LAO) while enabling real-time modification to the shape used to clip the volume. The main idea of the algorithm is that clipping only affects the ambient value of a small number of voxels, so by identifying these voxels and recalculating the ambient factor only for them, it is possible to significantly increase the rendering performance (by 2-5x) without decreasing the quality of the rendered image. Due to its fast performance, the algorithm is suitable for interactive environments where clipping changes could occur every frame. Additionally, the algorithm does not have any stereoscopic inconsistency, which makes it suitable for mixed reality environments. This paper is an extended version of the "Contextual Ambient Occlusion" article presented during the 2023 Graphics Interface conference, and includes, among other additions, the source code of the algorithm. [Display omitted] • Clipping only affects the ambient value of a small number of voxels. • It is more efficient to perform recalculation only on voxels affected by clipping. • Contextual Ambient Occlusion achieves same quality as Local Ambient Occlusion. • Contextual Ambient Occlusion is suitable for interactive environments. [ABSTRACT FROM AUTHOR]

Generating talking lips in sync with input speech has the potential to enhance speech communication and enable novel applications. This paper presents a system that can generate accurate 3D talking lips, readily applicable to unseen subjects and different languages. The developed head-mounted facial acquisition device and automated data processing pipeline can generate precise landmarks while mitigating the difficulty of acquiring 3D facial data. Our system consists of three stages to generate accurate lip movements. In the first stage, the fine-tuned Wav2Vec2.0+Transformer captures long-range audio context dependencies. In the second stage, we propose the Viseme Fixing method, which significantly improves lip accuracy at/b//p//m//f/ phonemes. In the last stage, we innovatively use the structural relationship between the inner and outer lips and learn to map the outer lip landmarks to the inner lip landmarks. Subjective evaluations show that the generated talking lips match the input audio significantly. We demonstrate two applications that animate 2D face videos and 3D face models using our landmarks. The precise lip landmarks allow the generated animations to exceed the results of state-of-the-art methods. [Display omitted] • Generate accurate 3D talking lips for unseen subjects and different languages. • Develop a head-mounted facial acquisition device to acquire precise 3D facial data. • Propose Viseme Fixing algorithm to enhance the lip accuracy of /b//p//m//f/ visemes. • Make two applications that bridge 3D landmarks to talking videos and talking meshes. [ABSTRACT FROM AUTHOR]

This paper presents a simple yet practical network architecture, ProLiF (Pro gressively-connected Li ght F ield network), for the efficient differentiable view synthesis of complex forward-facing scenes in both the training and inference stages. The progress of view synthesis has advanced significantly due to the recent Neural Radiance Fields (NeRF). However, when training a NeRF, hundreds of network evaluations are required to synthesize a single pixel color, which is highly consuming of device memory and time. This issue prevents the differentiable rendering of a large patch of pixels in the training stage for semantic-level supervision, which is critical for many practical applications such as robust scene fitting, style transferring, and adversarial training. On the contrary, our proposed simple architecture ProLiF, encodes a two-plane light field, which allows rendering a large batch of rays in one training step for image- or patch-level losses. To keep the multi-view 3D consistency of the neural light field, we propose a progressive training strategy with novel regularization losses. We demonstrate that ProLiF has good compatibility with LPIPS loss to achieve robustness to varying light conditions, and NNFM loss as well as CLIP loss to edit the rendering style of the scene. [Display omitted] • Introduction of ProLiF, a simple and efficient network architecture for differentiable view synthesis. • Development of a progressive training strategy with novel regularization losses to ensure multi-view 3D consistency. • Demonstration of ProLiF's compatibility with various loss functions for enhanced robustness and style editing capabilities. [ABSTRACT FROM AUTHOR]

The primary goal behind the system presented in this paper is to investigate the efficacy of using virtual reality (VR) for showcasing sizable medical equipment. Specifically, we focused on a mobile magnetic resonance imaging (MRI) scanner mounted on a truck trailer. The latter is integral to the mobile MRI setup and must be presented as part of the immersive experience. Therefore, we not only have to depict the medical apparatus but also provide the means of understanding its surroundings. This is especially important to radiologists and other medical personnel to ascertain if a given mobile medical facility fulfills their needs and wants. Furthermore, despite such MRI devices being designed for mobility, their long-distance transportation can be time-consuming, troublesome and expensive. Therefore, we can observe the need for showcasing such mobile MRI units without additional cost and burden related to transportation. To achieve this, we designed an immersive environment in which the users can interact with the real-life scale 3D model of a mobile MRI. In addition, we also verified the usability and expressiveness of our system using established heuristical approaches. [Display omitted] • Interactive VR presentations of medical equipment using high-fidelity MRI model. • Close-to-natural interaction based on hand-tracking supports the feel of presence. • Signal analysis for reasoning about visualization system components and structure. • Usability and expressiveness of VR environment verified through a heuristic approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach that acquires camera parameters and generates an integrated 3D joint using an RGB-D camera network distributed in an arbitrary location in space. The proposed technique consists of three steps. In the first step, camera parameters are calculated using partial joints as feature points. The internal and external parameters between cameras are not calculated using a specially manufactured calibration plate. In the second step, a 3D joint set is estimated by integrating the joints obtained from each camera using the calculated camera parameters. At the same time, a 3D volumetric model in the form of a point cloud is reconstructed. The third step consists of a joint correction algorithm. The resultant 3D joint with high reliability is estimated by correcting the position of the joint using the 3D point cloud reconstructed previously. The generated 3D joint can accurately express the shape and movement of 3D human. The estimated 3D joint was compared with the joint measured using a motion capture device to evaluate its performance. The temporal standard deviation between two measurements shows very low value from 1.966 mm to 7.99 mm. [Display omitted] • Performing camera calibration using incomplete or partial joints obtained from images output from cameras installed in irregular positions. • Temporal improvement of the precision of transformation parameters obtained through camera calibration. • 3D joint generation while minimizing the error of pose estimation algorithm. • Correction of 3d joint using 3d point cloud registration. • Simultaneous 3D reconstruction of 3D indoor space and humans. [ABSTRACT FROM AUTHOR]

To display images on panels and screens of different dimensions, there is a need to create algorithms that help adjust them to desired sizes through image retargeting (IR). In this sense, choosing the right algorithms seems to be a challenge. From this perspective, this paper was to propose a method for the assessment of retargeted images using deep learning (DL) models. To this end, the Conditional Random Fields as Recurrent Neural Networks (CRF-RNN) approach was applied to find image pixels in terms of importance and, the You-Only-Look-Once (YOLO) model was employed to identify semantic objects of images. As well, image patch similarity was computed based on Siamese Neural Network (SNN), and output image distortion was recognized by the CNN model. With reference to three well-known databases, viz., MIT RetargetMe, NRID and CUHK, the assessment results of the proposed algorithm demonstrated its superior usage as compared to the existing ones. [Display omitted] • Operating an R-CNN model to extract image importance maps. • Using a pre-trained YOLO to extract regions containing important image objects. • Training SNN to compute corresponding patch-similarity of original and retargeted images. • Training regression-based CNN to measure distortion of retargeted images based on pixel displacement patterns. • Merging measures using Gaussian Process Regression (GPR) and calculating the final measure. [ABSTRACT FROM AUTHOR]

Understanding and analyzing univariate distributions of data in terms of their shapes as well as their specific characteristics, regarding gaps, spikes, or outliers, is crucial in many scientific disciplines. In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., "accumulated stripes") design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks' Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes' design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes' design outperformed the line charts in accuracy for all study tasks. [Display omitted] • Novel design space to represent and compare univariate data distributions. • A subspace is introduced combining color-coded stripes with density lines. • The stripes' width and coloring are defined by adaptive and uniform binning methods. • Evaluation and validation of the subspace through two crowdsource studies. [ABSTRACT FROM AUTHOR]

Point cloud registration aligns point clouds through transformation or deformation. The existing advanced registration methods perform poorly in registering under various perturbations, especially for large rotations and small overlaps. Moreover, these methods cannot achieve rigid and non-rigid registrations simultaneously. In this paper, we propose a two-step generative optimization (GO) method to achieve registration, which alleviates the constraints of transformation representation and completely circumvents the matching of correspondences, making it suitable for rigid registration with large rotations and small overlaps, as well as for non-rigid registration. GO achieves registration by generating a pseudo point cloud approximating the target point cloud. The pseudo point cloud is iteratively deformed to approach the target point cloud through an estimation step (ES) and an updating step (US). The ES step generates initial pseudo point clouds in three different views and estimates their deformation via the extracted features decoding the similarity between the pseudo point clouds and the target point cloud. The US step calculates regressors to update the current estimations, generating new pseudo point clouds closer to the target point cloud. The final pseudo point clouds generated are averaged to attain the registration result. We evaluate the performance of GO for rigid and non-rigid registrations through the comparison with seven deep learning-based methods on eight datasets, consisting of both 3D and 2D data and ranging from synthetic to real scenes. Our experimental results demonstrate the high robustness and stability of GO in rigid registrations and illustrate the feasibility and high accuracy for non-rigid registrations. [Display omitted] Given a source point cloud S and a target point cloud M, GO aims to register S to M by iteratively generating different pseudo point clouds P t S, P t F, P t O, and making each generated pseudo point cloud approach the target model M to mimic the deformation of the source model S. The registration result S T is the average of the generated pseudo point clouds at the final iteration. Specifically, the GO iteration alternates between performing an estimation (ES) step and an updating (US) step. The former aims to estimate the coordinates of the newly generated pseudo point clouds in different views via the extracted feature of the existing pseudo point clouds. The latter intends to update the existing pseudo point clouds for generating new pseudo point clouds by making the current estimation approach the target point cloud. The generated pseudo point clouds are then used to estimate the coordinates of new pseudo point clouds in the next ES step. GO terminates the iteration once the training error is below a threshold. And the generated pseudo point clouds are averaged to attain the final registration result simultaneously. • A novel two-step generative optimization (GO) is proposed for rigid and non-rigid registration. • GO outperforms both traditional and deep learning methods in robustness and accuracy. • The generation of pseudo models expedites the convergence of GO. [ABSTRACT FROM AUTHOR]

Point cloud registration precision degrades when the scene contains symmetric structures or repeated patches. Also, the common operation of continuous downsampling in existing registration methods causes the loss of detailed information and leads to further registration precision degradation. In this paper, we propose a high precision point cloud registration network (HPNet) to learn discriminative features by considering both multi-scale information and position information. We first propose a multi-scale feature extraction module similar to the feature pyramid that allows the extracted features to contain multi-scale information. Then, an information interaction is performed on the features to learn global contextual information by using Transformer with a hybrid position encoding, which takes into account both absolute and relative positions of points. Finally, the features obtained from the information interaction module are directly used to predict the point correspondences. Comprehensive experiments on 3DMatch, 3DLoMatch, ModelNet, and ModelLoNet datasets demonstrate the state-of-the-art performance of the proposed method and the implementation of HPNet in SLAM shows its effectiveness in real application. [Display omitted] • Point cloud registration loses detail information during continuous downsampling. • Feature fusion of low-level detail information and high-level semantic information. • Hybrid position encoding consider both absolute and relative positions. positions. • Point cloud registration algorithms applied to SLAM. [ABSTRACT FROM AUTHOR]

Real point cloud registration, involving homologous and cross-source 3D data, poses significant challenges such as partial overlap, high noise, density disparities, and scale variations. In this paper, we introduce a framework called OEFM to guide transform estimation on the basis of feature metrics through overlap estimation with overlap region center weighting, which estimates transform parameters by minimizing feature projection error on overlap region features. Our proposed approach utilizes an overlap filtering module with a center-weighting mechanism to filter overlap points and feeds these points into a feature metric framework with a forward–backward transformation to estimate the transformation parameters. The rationale behind our approach is that only overlapping regions are useful for point cloud registration, while non-overlapping regions are anomalous. Furthermore, our proposed approach requires no correspondence search, making it robust to partial overlaps, large noise, density differences, and scale variations. We show that our approach achieves the highest registration recall for 3DMatch and 3DCSR in extensive experiments on both homologous and cross-source datasets. • We present an efficient unsupervised training module for transform estimation with forward–backward transformation, which could enhance the performance of unsupervised training as well as transform estimation. • We provide a robust point cloud registration technique combined with feature matching, without using the common correspondence search. [ABSTRACT FROM AUTHOR]

The ability to ease operation and real-time feedback to image editing has attracted increasing attention in our everyday lives. Existing edit propagation approaches typically formulate the color editing task as a quadratic energy optimization problem, which is computationally expensive and requires a lot of user interactions. To address this problem, in this paper, we propose a novel edit propagation method based on color palette. We first extract a color palette from the input image, and then calculate the mixing weights of the image pixels with respect to the extracted palette. Finally, we solve for an edited palette according to user edits, to propagate local edits to the whole image. Our main contributions include formulating the edit propagation as a palette-based optimization problem that can be solved efficiently, and requiring only fewer user interactions than existing methods. We have demonstrated our method for color editing on a wide range of examples. Compared to existing methods, our approach is more efficient and friendly for novice users due to its ease of interaction. [Display omitted] • We presented a palette-based edit propagation for pixel-level color editing. • We developed a graphical user interface (GUI) that is easy to use and learn. • Our approach is efficient and requires fewer interactions than previous methods. [ABSTRACT FROM AUTHOR]