Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds

• URL: http://arxiv.org/abs/2101.02381v1
• Date: Thu, 7 Jan 2021 05:38:19 GMT
• Title: Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds
• Authors: Jingyu Gong, Jiachen Xu, Xin Tan, Jie Zhou, Yanyun Qu, Yuan Xie, Lizhuang Ma
• Abstract summary: Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation. We propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundary-aware Geometric. GEM is designed to encode geometric information and aggregate features with discrimination in a neighborhood.
• Score: 45.270215729464056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects. In this paper, firstly, we propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundary-aware Geometric Encoding Module (GEM) is designed to encode geometric information and aggregate features with discrimination in a neighborhood, so that the local features belonging to different categories will not be polluted by each other. To provide extra geometric information for boundary-aware GEM, we also propose a light-weight Geometric Convolution Operation (GCO), making the extracted features more distinguishing. Built upon the boundary-aware GEM, we build our network and test it on benchmarks like ScanNet v2, S3DIS. Results show our methods can significantly improve the baseline and achieve state-of-the-art performance. Code is available at https://github.com/JchenXu/BoundaryAwareGEM.

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