Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation

Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 10433-10441

Abstract


We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting point labels, we construct an edge branch to hierarchically integrate point features and generate edge features. To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process. For each edge in the final graph, we predict a label to indicate the semantic consistency of the two connected points to enhance point prediction. At different layers, edge features are also fed into the corresponding point module to integrate contextual information for message passing enhancement in local regions. The two branches interact with each other and cooperate in segmentation. Decent experimental results on several 3D semantic labeling datasets demonstrate the effectiveness of our work.

Related Material


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[bibtex]
@InProceedings{Jiang_2019_ICCV,
author = {Jiang, Li and Zhao, Hengshuang and Liu, Shu and Shen, Xiaoyong and Fu, Chi-Wing and Jia, Jiaya},
title = {Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}