3D Computer Vision | 3D Point Cloud Processing

This is an add-on lecture to the CS4277/CS5477 - 3D Computer Vision course at the School of Computing at NUS.

These are the links to the papers mentioned in this lecture:

1. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, CVPR 2017 (https://arxiv.org/abs/1612.00593)
2. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, NeurIPS 2017 (https://arxiv.org/abs/1706.02413)
3. PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018 (https://arxiv.org/abs/1804.03492)
4. USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds, ICCV 2019 (https://arxiv.org/abs/1904.00229)
5. 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration, ECCV 2018 (https://arxiv.org/abs/1807.09413)
6. Deep Hough Voting for 3D Object Detection in Point Clouds, ICCV 2019 (https://arxiv.org/abs/1904.09664)
7. Weakly Supervised Semantic Point Cloud Segmentation: Towards 10× Fewer Labels, CVPR 2020 (https://arxiv.org/abs/2004.04091)
8. RPM-Net: Robust Point Matching using Learned Features, CVPR2020 (https://arxiv.org/abs/2003.13479)
9. DeepI2P: Image-to-Point Cloud Registration via Deep Classification, CVPR 2021 (https://arxiv.org/abs/2104.03501)

Lecturer: Gim Hee Lee (https://www.comp.nus.edu.sg/~leegh/)
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Disclaimer: This video lecture is provided freely for your reference. The lecturer and NUS are not responsible for anything expressed herein.