High-Performance Feature Extraction Network for Point Cloud Semantic Segmentation

Abstract

The key to point cloud semantic segmentation lies in the efficient extraction of features from the point cloud data. However, previous research has often suffered from the ineffective capture of fine-grained spatial features of points or issues with ambiguous regional feature representation. To address this problem, We propose a method for point cloud surface construction to extract fine local geometric topology. We then embed the surface topology into each feature aggregation process to enrich feature representation, and propose a novel feature slice extraction method to capture significant local geometric features and contextual information. Furthermore, to enhance the performance of the Transformer network, we employ neighborhood grouping and double convolution operations at the initial network layer to aggregate the raw features of the point cloud. Numerous comparative experiments prove the effectiveness of the method in this letter, and we achieve state-of-the-art performance with mIoU of 74.7% on ScanNet V2 and 73.7% on S3DIS Area5.