Abstract
We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds. The network architecture capitalizes on the sparse nature of irregular point clouds,and hierarchically coarsens the data representation with space partitioning. At the same time, the proposed spherical kernels systematically quantize point neighborhoods to identify local geometric structures in the data, while maintaining the properties of translation-invariance and asymmetry. We specify spherical kernels with the help of network neurons that in turn are associated with spatial locations.We exploit this association to avert dynamic kernel generation during network training that enables efficient learning with high resolution point clouds. The effectiveness of the proposed technique is established on the benchmark tasks of 3D object classification and segmentation, achieving competitive performance on ShapeNet and RueMonge2014 datasets.
Original language | English |
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Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019) |
Place of Publication | USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 9623-9632 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-7281-3293-8 |
DOIs | |
Publication status | Published - Jun 2019 |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2019 - Long Beach Convention & Entertainment Center, Long Beach, United States Duration: 16 Jun 2019 → 20 Jun 2019 http://cvpr2019.thecvf.com/ |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 2019-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2019 |
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Abbreviated title | CVPR 2019 |
Country/Territory | United States |
City | Long Beach |
Period | 16/06/19 → 20/06/19 |
Internet address |