Aiming to solve the problem that spatially distributed similar instances cannot be distinguished in 3D point cloud instance segmentation, a 3D point cloud instance segmentation network, considering the global shape contour, was proposed. This research used the global-to-local design idea and added the global shape constraint to solve this problem. A Transformer module (Global Shape Attention, GSA) that can capture the shape contour information of the instance in the scene was designed. This module encoded the shape contour information into the Transformer structure as a Key-Value and extracted the instance fused with the global shape contour features, for instance, segmentation. At the same time, the network directly predicted the instance mask in an end-to-end mode, avoiding heavy post-processing algorithms. Many experiments have been conducted on the S3DIS, ScanNet, and STPL3D datasets, and our experimental results showed that our proposed network can efficiently capture the shape contour information of scene instances and can help to alleviate the problem of the difficulty distinguishing between spatially distributed similar instances in a scene, improving the efficiency and stability of instance segmentation.

The study of convolutional neural networks for 3D point clouds is becoming increasingly popular, and the difficulty lies mainly in the disorder and irregularity of point clouds. At present, it is straightforward to propose a convolution operation and perform experimental validation. Although good results are achieved, the principles behind them are not explained—i.e., why this can solve the disorder and irregularity of point clouds—and it is difficult for the researchers to design a point cloud convolution network suitable for their needs. For this phenomenon, we propose a point convolution network framework based on spatial location correspondence. Following the correspondence principle can guide us in designing convolution networks adapted to our needs. We analyzed the intrinsic mathematical nature of the convolution operation, and we argue that the convolution operation remains the same when the spatial location correspondence between the convolution kernel points and the convolution range elements remains unchanged. Guided by this principle, we formulated a general point convolution framework based on spatial location correspondence, which explains how to handle a disordered point cloud. Moreover, we discuss different kinds of correspondence based on spatial location, including M-to-M, M-to-N, and M-to-1 relationships, etc., which explain how to handle the irregularity of point clouds. Finally, we give the example of a point convolution network whose convolution kernel points are generated based on the sample’s covariance matrix distribution according to our framework. Our convolution operation can be applied to various point cloud processing networks. We demonstrated the effectiveness of our framework for point cloud classification and semantic segmentation tasks, achieving competitive results with state-of-the-art networks.