Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF

Sensors

Zairong Wang¹  Menghan Zhang²  Junmin Xue²  Yunbo Rao²  Jiansu Pu²  Zhanglin Cheng³ 

[1] School of Computer Science, Neijiang Normal University, Neijiang 641100, China;School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;

关键词: deep learning;    3D point cloud;    deep neural network;    semantic segmentation;    DenseCRF;   

DOI  :  10.3390/s21082731

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown