Learning Scale-Adaptive Representations for Point-Level LiDAR Semantic Segmentation


Tongfeng Zhang, Kaizhi Yang and Xuejin Chen


The massive objects with various scales and categories in autonomous driving scenes pose a great challenge to the LiDAR semantic segmentation task. Although the voxel-based 3d convolutional networks employed by existing state-of-the-art methods can extract features with different spatial scales, they cannot conduct effective discrimination and combination on them. In this paper, we propose a Scale-Adaptive Fusion (SAF) module that can progressively and selectively fuse features with different receptive fields to help the network deal with scale variations across objects adaptively. In addition, we propose a novel Local Point Refinement (LPR) module to address the quantization loss problem of voxel-based methods. It could take the geometric structure of original point cloud into account by converting voxel-wise feature to the point-wise one. Our proposed method is evaluated on three public datasets, i.e., SemanticKITTI, SemanticPOSS and nuScenes dataset and achieves competitive performance.

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  Important Dates

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Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021