We introduce a novel algorithm for isotropic surface remeshing which progressively eliminates obtuse triangles and improves small angles. The main novelty of the proposed approach is a simple vertex insertion scheme that facilitates the removal of large angles, and a vertex removal operation that improves the distribution of small angles. In combination with other standard local mesh operators, e.g., connectivity optimization and local tangential smoothing, our algorithm is able to remesh efficiently a low-quality mesh surface. Our approach can be applied directly or used as a post-processing step following other remeshing approaches. Our method has a similar computational efficiency to the fastest approach available, i.e., real-time adaptive remeshing [1]. In comparison with state-of-the-art approaches, our method consistently generates better results based on evaluations using different metrics.

Multi-view Stereo (MVS) aims to reconstruct a 3D point cloud model from multiple views. In recent years, learning-based MVS methods have received a lot of attention and achieved excellent performance compared with traditional methods. However, these methods still have apparent shortcomings, such as the accumulative error in the coarse-to-fine strategy and the inaccurate depth hypotheses based on the uniform sampling strategy. In this paper, we propose the NR-MVSNet, a coarse-to-fine structure with the depth hypotheses based on the normal consistency (DHNC) module, and the depth refinement with reliable attention (DRRA) module. Specifically, we design the DHNC module to generate more effective depth hypotheses, which collects the depth hypotheses from neighboring pixels with the same normals. As a result, the predicted depth can be smoother and more accurate, especially in texture-less and repetitive-texture regions. On the other hand, we update the initial depth map in the coarse stage by the DRRA module, which can combine attentional reference features and cost volume features to improve the depth estimation accuracy in the coarse stage and address the accumulative error problem. Finally, we conduct a series of experiments on the DTU, BlendedMVS, Tanks & Temples, and ETH3D datasets. The experimental results demonstrate the efficiency and robustness of our NR-MVSNet compared with the state-of-the-art methods. Our implementation is available at https://github.com/wdkyh/NR-MVSNet.

Panoramic depth estimation has become a hot topic in 3D reconstruction techniques with its omnidirectional spatial field of view. However, panoramic RGB-D datasets are difficult to obtain due to the lack of panoramic RGB-D cameras, thus limiting the practicality of supervised panoramic depth estimation. Self-supervised learning based on RGB stereo image pairs has the potential to overcome this limitation due to its low dependence on datasets. In this work, we propose the SPDET, an edge-aware self-supervised panoramic depth estimation network that combines the transformer with a spherical geometry feature. Specifically, we first introduce the panoramic geometry feature to construct our panoramic transformer and reconstruct high-quality depth maps. Furthermore, we introduce the pre-filtered depth-image-based rendering method to synthesize the novel view image for self-supervision. Meanwhile, we design an edge-aware loss function to improve the self-supervised depth estimation for panorama images. Finally, we demonstrate the effectiveness of our SPDET with a series of comparison and ablation experiments while achieving the state-of-the-art self-supervised monocular panoramic depth estimation. Our code and models are available at https://github.com/zcq15/SPDET .

Point cloud registration is a basic task in computer vision and computer graphics. Recently, deep learning-based end-to-end methods have made great progress in this field. One of the challenges of these methods is to deal with partial-to-partial registration tasks. In this work, we propose a novel end-to-end framework called MCLNet that makes full use of multi-level consistency for point cloud registration. First, the point-level consistency is exploited to prune points located outside overlapping regions. Second, we propose a multi-scale attention module to perform consistency learning at the correspondence-level for obtaining reliable correspondences. To further improve the accuracy of our method, we propose a novel scheme to estimate the transformation based on geometric consistency between correspondences. Compared to baseline methods, experimental results show that our method performs well on smaller-scale data, especially with exact matches. The reference time and memory footprint of our method are relatively balanced, which is more beneficial for practical applications.

Point cloud primitive instance segmentation is critical for understanding the geometric shapes of man-made objects. Existing learning-based methods mainly focus on learning high-dimensional feature representations of points and further perform clustering or region growing to obtain corresponding primitive instances. However, these features generally cannot accurately represent the discriminability between instances, especially near the boundaries or in regions with small differences in geometric properties. This limitation often leads to over- or under-segmentation of geometric primitives. On the other hand, the boundaries of different primitives are the direct features that distinguish them and thus utilizing boundary information to guide feature learning and clustering is crucial for this task. In this paper, we propose a novel framework BGPSeg for point cloud primitive instance segmentation that utilizes boundary-guided feature extraction and clustering. Specifically, we first introduce a boundary-guided feature extractor with the additional input of a boundary probability map, which utilizes boundary-guided sampling and a boundary transformer to enhance feature discrimination among points crossing geometric boundaries. Furthermore, we propose a boundary-guided primitive clustering module, which combines boundary clues and geometric feature discrimination for clustering to further improve the segmentation performance. Finally, we demonstrate the effectiveness of our BGPSeg with a series of comparison and ablation experiments while achieving the state-of-the-art primitive instance segmentation. Our code is available at https://github.com/fz-20/BGPSeg.

Existing GAN-based generative methods are typically used for semantic image synthesis. We pose the question of whether GAN-based architectures can generate plausible depth maps and find that existing methods have difficulty in generating depth maps which reasonably represent 3D scene structure due to the lack of global geometric correlations. Thus, we propose DepthGAN, a novel method of generating a depth map using a semantic layout as input to aid construction, and manipulation of well-structured 3D scene point clouds. Specifically, we first build a feature generation model with a cascade of semantically-aware transformer blocks to obtain depth features with global structural information. For our semantically aware transformer block, we propose a mixed attention module and a semantically aware layer normalization module to better exploit semantic consistency for depth features generation. Moreover, we present a novel semantically weighted depth synthesis module, which generates adaptive depth intervals for the current scene. We generate the final depth map by using a weighted combination of semantically aware depth weights for different depth ranges. In this manner, we obtain a more accurate depth map. Extensive experiments on indoor and outdoor datasets demonstrate that DepthGAN achieves superior results both quantitatively and visually for the depth generation task.