At present, effectively aggregating and transferring the local features of point cloud is still an unresolved technological conundrum. In this study, we propose a new space-cover convolutional neural network (SC-CNN) for tasks such as point cloud classification and segmentation. The core of this network is space-cover convolution (SC-Conv), which implements depthwise separable convolution on the point cloud. In addition, a newly designed space-cover operator (SCOP) replaces depthwise convolution. The key to SC-Conv is constructing anisotropic spatial geometry in the local point cloud. The SCOP achieves this by utilizing the positional and feature relationships to learn the high-order relationship expression between points. First, data-driven adaptive learning from the 3-D coordinate relationship between the local points is used to determine the weight of the SCOP. Then, the edge feature of the neighboring point relative to the sampling point is used as the input of the SCOP. Finally, a deformable spatial geometry is constructed in the feature space between local points to aggregate the local high-order features. By stacking SC-Conv to construct SC-CNN with a hierarchical network structure for point cloud analysis, we can better perceive the shape information of point cloud and improve network robustness. Finally, we provide numerous experiments to verify that SC-CNN parallels or even outperforms advanced methods in shape classification, part segmentation, and large-scale indoor scene segmentation tasks. The open-source code was published at https://github.com/changshuowang/SC-CNN .

Recent development in computing power has resulted in performance improvements on holistic(none-occluded) person Re-Identification (ReID) tasks. Nevertheless, the precision of the recent research will diminish when a pedestrian is obstructed by obstacles. Within the realm of 2D space, the loss of information from obstructed objects continues to pose significant challenges in the context of person ReID. Person is a 3D non-grid object, and thus semantic representation learning in only 2D space limits the understanding of occluded person. In the present work, we propose a network based on 3D multi-view learning, allowing it to acquire geometric and shape details of an occluded pedestrian from 3D space. Simultaneously, it capitalizes on advancements in 2D-based networks to extract semantic representations from 3D multi-views. Specifically, the surface random selection strategy is proposed to convert images of 2D RGB into 3D multi-views. Using this strategy, we build four extensive 3D multi-view data collections for person ReID. After that, Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View Learning(MV-3DSReID), is proposed for identifying the person by learning person geometry and structure representation from the groups of multi-view images. In comparison to alternative data formats (e.g., 2D RGB, 3D point cloud), multi-view images complement each other’s detailed features of the 3D object by adjusting rendering viewpoints, thus facilitating a more comprehensive understanding of the person for both holistic and occluded ReID situations. Experiments on occluded and holistic ReID tasks demonstrate performance levels comparable to state-of-the-art methods, validating the effectiveness of our proposed approach in tackling challenges related to occlusion. The code is available at https://github.com/hangjiaqi1/MV-TransReID.