MSSA-Net: A Multi-Scale Structure-Aware Network for Edge Detection in Point Clouds

Edge detection in point clouds is a fundamental problem in 3D vision. Previous approaches based on point-based neural networks treat edge detection as a binary segmentation task, directly applying semantic segmentation frameworks. However, due to the intrinsic differences between edge detection and semantic segmentation, these methods have limitations compared to traditional methods that rely on hand-crafted features, as well as recent volumetric edge representation methods. To address these challenges, we propose three key constraints: Local Structure Disruption, Encoding Region Offset, and Information Loss in Feature Propagation. Based on these constraints, we introduce a novel Multi-Scale Structure-Aware Network (MSSA-Net). MSSA-Net is designed around local multi-scale neighborhoods, learning intact local structural features at different scales within highly relevant neighborhoods for each point. This strategy effectively avoids Local Structure Disruption and Encoding Region Offset. During feature learning, the MSSA Core module generates adaptive encoding branches for different scales of neighborhoods. The learned multi-scale local structural features are then concatenated to encode the local structural information of each point in the point cloud. MSSA-Net eliminates the need for propagating high-level features to low-level features, thereby preventing Information Loss in Feature Propagation. Extensive experiments demonstrate that our MSSA-Net surpasses existing works by a large margin and achieves state-of-the-art performance on various datasets.