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Estimating 3D hand shape from a single-view RGB image is important for many applications. However, the diversity of hand shapes and postures, depth ambiguity, and occlusion may result in pose errors and noisy hand meshes. Making full use of 2D cues such as 2D pose can effectively improve the quality of 3D human hand shape estimation. In this paper, we use 2D joint heatmaps to obtain spatial details for robust pose estimation. We also introduce a depth-independent 2D mesh to avoid depth ambiguity in mesh regression for efficient hand-image alignment. Our method has four cascaded stages: 2D cue extraction, pose feature encoding, initial reconstruction, and reconstruction refinement. Specifically, we first encode the image to determine semantic features during 2D cue extraction; this is also used to predict hand joints and for segmentation. Then, during the pose feature encoding stage, we use a hand joints encoder to learn spatial information from the joint heatmaps. Next, a coarse 3D hand mesh and 2D mesh are obtained in the initial reconstruction step; a mesh squeeze-and-excitation block is used to fuse different hand features to enhance perception of 3D hand structures. Finally, a global mesh refinement stage learns non-local relations between vertices of the hand mesh from the predicted 2D mesh, to predict an offset hand mesh to fine-tune the reconstruction results. Quantitative and qualitative results on the FreiHAND benchmark dataset demonstrate that our approach achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

With the widespread availability and utilization of 3D meshes across various applications, the need for accurately assessing their visual quality has become increasingly important. Despite the significance of this task, the literature offers few No-Reference (NR) approaches for evaluating the visual quality of 3D meshes. In response to this gap, this paper proposes a novel NR approach tailored specifically to score the quality of 3D meshes. After rendering a 3D mesh into 2D views and patches, a pre-trained convolutional neural network automatically extracts deep features from. These features are then employed in a Multi-Layer Perceptron regressor to predict the quality score of the rendered images. The obtained scores are combined with their corresponding BRISQUE scores, and an additional MLP regressor is used to predict the final score. We present experimental results demonstrating the effectiveness and robustness of our approach across a diverse range of 3D mesh datasets. Comparative analyses with existing NR methods underscore the superior performance and versatility of the proposed approach. Overall, this paper contributes to the advancement of NR techniques for assessing 3D mesh quality, offering a valuable tool for researchers, practitioners, and developers working with 3D models across various domains.

Inspection and documentation of masonry structures is a time-consuming and expensive process that heavily relies on an engineer's expertise. This paper introduces a computer vision-based method that automates the creation of classified point clouds from 3D reconstruction models obtained through photogrammetry and/or laser scanning. By leveraging Convolutional Neural Networks (CNN) on 3D renders, the proposed methodology can be used to classify structural features (i.e., blocks, mortar, cracks) with greater accuracy and less effort than conventional methods. Moreover, this approach is flexible and can include further classifications by incorporating additional CNN models, allowing broader applications across various materials (i.e., concrete, steel, timber) and defects. Additionally, a precise methodology for accurate crack quantification featuring a manual annotation tool was introduced which was validated using outputs from a full-scale masonry arch bridge test carried out in the laboratory. The results emphasize the robustness of the classification approach and highlight the useful geometric information that can be gained from full-scale masonry structures. The proposed approach has the potential to use image data from UAVs/static cameras and revolutionize the way we document and inspect structures in the future. • Classification of 3D point-cloud of masonry fabric via semantic-segmentation on reality-mesh models. • Automatic detection of cracks from 3D reconstruction models of masonry structures • Quantification of the extend of detected cracks in 3D space. • Comparison of 3D photogrammetry software using a case study of an experimental masonry arch bridge. [ABSTRACT FROM AUTHOR]