LAPTRAN: TRANSFORMER EMBEDDING GRAPH LAPLACIAN FOR POINT CLOUD PART SEGMENTATION

Since the feature representations of the points located at the junction regions of various parts are ambiguous, it is still challenging to exploit the fine-grained semantic features of point clouds on part segmentation tasks. To resolve the issue, we design a modified transformer module, named Laplacian transformer, to investigate the local differences between each point and its corresponding neighbors based on graph Laplacian theory. This module constructs a more accurate local geometric representation of the point cloud. It concentrates on the points located at the junction areas of various parts while boosting the recognition effect of these points. Encapsulated with the Laplacian module, we propose a Unet-like transformer framework to perform part segmentation for point clouds. Experimental results demonstrate that the proposed framework achieves more accurate results on public benchmark datasets.