LSGRNet: Local Spatial Latent Geometric Relation Learning Network for 3D point cloud semantic segmentation

Abstract

In recent years, remarkable ability has been demonstrated by the Transformer model in capturing remote dependencies and improving point cloud segmentation performance. However, localized regions separated from conventional sampling architectures have resulted in the destruction of structural information of instances and a lack of exploration of potential geometric relationships between localized regions. To address this issue, a Local Spatial Latent Geometric Relation Learning Network (LSGRNet) is proposed in this paper, with the geometric properties of point clouds serving as a reference. Specifically, spatial transformation and gradient computation are performed on the local point cloud to uncover potential geometric relationships within the local neighborhood. Furthermore, a local relationship aggregator based on semantic and geometric relationships is constructed to enable the interaction of spatial geometric structure and information within the local neighborhood. Simultaneously, boundary interaction feature learning module is employed to learn the boundary information of the point cloud, aiming to better describe the local structure. The experimental results indicate that excellent segmentation performance is exhibited by the proposed LSGRNet in benchmark tests on the indoor datasets S3DIS and ScanNetV2, as well as the outdoor datasets SemanticKITTI and Semantic3D.