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3D Convolutional Neural Network for Semantic Scene Segmentation based on Unstructured Point Clouds

Volume 14, Number 7, July 2018, pp. 1503-1512
DOI: 10.23940/ijpe.18.07.p14.15031512

Rui Zhanga,b, Yan Wangc, Guangyun Lib, Zhen Hana, Junpeng Lia, and Chunying Lia

aNorth China University of Water Resources and Electric Power, Zhengzhou, 450045, China
bInformation Engineering University, Zhengzhou, 450052, China
cZhengzhou Institute of Technology, Zhengzhou, 450044, China

(Submitted on March 19, 2018; Revised on April 23, 2018; Accepted on June 13, 2018)

Abstract:

The use of point cloud datasets is an inevitable trend in the analysis of natural scenes. In this paper, we propose a semantic segmentation network architecture that consumes 3D point clouds directly, which can efficiently avoid mapping 3D point clouds to 2D images. Experimental results indicate strong performance that is on par with or even better than state-of-the-art methods for semantic segmentation on the Stanford semantic parsing dataset.

 

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