Pooling Scores of Neighboring Points for Improved 3D Point Cloud Segmentation

3D point cloud segmentation has been rapidly advanced by exploring features of neighboring points. However, existing methods still suffer from ambiguous features, especially for the points in junction regions. In this paper, we show that such problem can be alleviated by utilizing the segmentation scores of neighboring points. We thus propose an attention-based score refinement module, which can be easily integrated with existing 3D point cloud segmentation networks and improve the segmentation accuracy. We demonstrate the effectiveness of our proposed method with extensive experiments on two challenging datasets (i.e., ShapeNet and ScanNet).