Semantic segmentation networks of 3D point clouds for RGB-D indoor scenes

Abstract

This paper focuses on the semantic segmentation networks of 3D point clouds for indoor scenes. We first reduce the PointNet structure to get a reduced point network (RPN) that achieves the same performance but has less training and evaluation time comparing with PointNet. Secondly, we propose two solutions to get scale invariance and robust test performance: one is modifying RPN to get the robust performance and adding stable multi-scaling layers (MPN); another is introducing a novel point-based network based on Angular coordinates instead of Euclidean coordinates for point representation (APN). The ablation study of our networks (RPN, MPN, APN) is done. Compared to state-of-the-art semantic segmentation networks based on 3D point clouds, the experimental results show that our MPN and APN networks both achieve higher training and evaluation accuracy, as well as mean intersection over union (IoU) and overall accuracy on two benchmarks. We also have better qualitative segmentation results when directly test on another benchmark indoor scenes as well as real corridor scenes from our robots RGB-D mapping.