Structural Relational Reasoning of Point Clouds

Yueqi Duan, Yu Zheng, Jiwen Lu, Jie Zhou, Qi Tian; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 949-958

Abstract


The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to reason for them. In this paper, we propose an effective plug-and-play module called the structural relation network (SRN) to reason about the structural dependencies of local regions in 3D point clouds. Existing network architectures on point sets such as PointNet++ capture local structures individually, without considering their inner interactions. Instead, our SRN simultaneously exploits local information by modeling their geometrical and locational relations, which play critical roles for our humans to understand 3D objects. The proposed SRN module is simple, interpretable, and does not require any additional supervision signals, which can be easily equipped with the existing networks. Experimental results on benchmark datasets indicate promising boosts on the tasks of 3D point cloud classification and segmentation by capturing structural relations with the SRN module.

Related Material


[pdf]
[bibtex]
@InProceedings{Duan_2019_CVPR,
author = {Duan, Yueqi and Zheng, Yu and Lu, Jiwen and Zhou, Jie and Tian, Qi},
title = {Structural Relational Reasoning of Point Clouds},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}