GeoNet: Deep Geodesic Networks for Point Cloud Analysis

Tong He, Haibin Huang, Li Yi, Yuqian Zhou, Chihao Wu, Jue Wang, Stefano Soatto; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6888-6897

Abstract


Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling. However, such connectivity information is lost in point clouds. Thus we introduce GeoNet, the first deep learning architecture trained to model the intrinsic structure of surfaces represented as point clouds. To demonstrate the applicability of learned geodesic-aware representations, we propose fusion schemes which use GeoNet in conjunction with other baseline or backbone networks, such as PU-Net and PointNet++, for down-stream point cloud analysis. Our method improves the state-of-the-art on multiple representative tasks that can benefit from understandings of the underlying surface topology, including point upsampling, normal estimation, mesh reconstruction and non-rigid shape classification.

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[bibtex]
@InProceedings{He_2019_CVPR,
author = {He, Tong and Huang, Haibin and Yi, Li and Zhou, Yuqian and Wu, Chihao and Wang, Jue and Soatto, Stefano},
title = {GeoNet: Deep Geodesic Networks for Point Cloud Analysis},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}