Self-Supervised Point Set Local Descriptors for Point Cloud Registration
@article{Yuan2021SelfSupervisedPS, title={Self-Supervised Point Set Local Descriptors for Point Cloud Registration}, author={Yijun Yuan and Jiawei Hou and A. N{\"u}chter and S. Schwertfeger}, journal={Sensors (Basel, Switzerland)}, year={2021}, volume={21} }
Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one… CONTINUE READING
Figures, Tables, and Topics from this paper
One Citation
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
- Computer Science
- Artificial Intelligence Review
- 2020
- 2
- PDF
References
SHOWING 1-10 OF 62 REFERENCES
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- Computer Science
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2020
- 11
- PDF
MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
- Computer Science
- ArXiv
- 2019
- 7
- PDF
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
- Computer Science
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
- 298
- Highly Influential
- PDF
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
- Computer Science
- ECCV
- 2018
- 91
- Highly Influential
- PDF
The Perfect Match: 3D Point Cloud Matching With Smoothed Densities
- Computer Science
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019
- 73
- PDF
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Computer Science
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
- 3,499
- Highly Influential
- PDF
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- Computer Science
- NIPS
- 2017
- 2,227
- PDF
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
- Computer Science
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- 2018
- 140
- PDF