Point cloud registration is a critical research area in computer vision with extensive applications. Recent studies have unveiled the significant potential of graph neural networks (GNNs) for point cloud registration. One key approach is to leverage the smoothness of graph convolutions to extract similarity information between points. However, as the number of convolution layers increases, the features between points tend to become consistent, and distinctiveness is always neglected, which contradicts point cloud registration. To this end, this paper presents a new GNN framework with 3D graph smoothing-sharpening convolution (GNN-GSSC) for point cloud registration. It includes two new convolutional strategies: graph smoothing convolution (SmoothGConv) and graph sharpening convolution (SharpGConv). The former utilizes Laplacian smoothing to aggregate similar information from neighbouring nodes, whereas the latter encourages each node to move away from its neighbours to obtain more discriminative information. Specifically, we calculate the difference information between the central node and neighbouring nodes to supplement the node feature information while aggregating the similarity information of the nodes. In addition, we devise a Transformer-based overlapping point scoring module, enhancing the emphasis on overlapping areas while weakening the focus on non-overlapping areas by scoring each point. Experiments reveal that the proposed method is optimal compared to other existing methods. More importantly, SharpGConv is a plug-and-play graph convolution module that is particularly advantageous for extracting distinctive information in point cloud registration.

3D object tracking in point clouds is still a challenging problem due to the sparsity of LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV tracker, which can significantly improve the tracking performance in sparse 3D point clouds. Specifically, it consists of a Siamese shape-aware feature learning network and a voxel-to-BEV target localization network. The Siamese shape-aware feature learning network can capture 3D shape information of the object to learn the discriminative features of the object so that the potential target from the background in sparse point clouds can be identified. To this end, we first perform template feature embedding to embed the template's feature into the potential target and then generate a dense 3D shape to characterize the shape information of the potential target. For localizing the tracked target, the voxel-to-BEV target localization network regresses the target's 2D center and the z-axis center from the dense bird's eye view (BEV) feature map in an anchor-free manner. Concretely, we compress the voxelized point cloud along z-axis through max pooling to obtain a dense BEV feature map, where the regression of the 2D center and the z-axis center can be performed more effectively. Extensive evaluation on the KITTI tracking dataset shows that our method significantly outperforms the current state-of-the-art methods by a large margin. Code is available at https://github.com/fpthink/V2B.