Fast 3D Point Cloud Segmentation Using Deep Neural Network

Abstract

Accurate segmentation of entity categories is the key step for 3D scene understanding. In this paper, we present a novel fast Deep Neural Network (DNN) model with Dense Conditional Random Field (DCRF) as a post-processing step, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on the labels of semantics, a novel DCRF model is elaborated to refine the result of segmentation. Besides, we apply optimization to the original point cloud, allowing the network to handle fewer points without any sacrifice to accuracy. In the experiment, our proposed method is comprehensively evaluated through four indicators, and achieves state-of-art performance.