Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 8; p. 2731
• Main Authors: Rao, Yunbo, Zhang, Menghan, Cheng, Zhanglin, Xue, Junmin, Pu, Jiansu, Wang, Zairong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.04.2021; MDPI
• Subjects: 3D point cloud; Accuracy; Conditional random fields; Deep learning; deep neural network; DenseCRF; Neural networks; Noise; Post-production processing; Scene analysis; semantic segmentation; Semantics; Sparsity; 3D point cloud; deep learning; semantic segmentation; DenseCRF; deep neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21082731

Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, 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 semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any sacrifice to accuracy, we apply optimization to the original data of the point cloud, allowing the network to handle fewer data. In the experiment, our proposed method is conducted comprehensively through four evaluation indicators, proving the superiority of our method.