SGP: Self-supervised Geometric Perception

[CVPR 2021 Oral] Self-supervised Geometric Perception https://arxiv.org/abs/2103.03114

Introduction

In short, SGP is, to the best of our knowledge, the first general framework for feature learning in geometric perception without any supervision from ground-truth geometric labels.

SGP runs in an EM fashion. It iteratively performs robust estimation of the geometric models to generate pseudo-labels, and feature learning under the supervision of the noisy pseudo-labels.

We applied SGP to camera pose estimation and point cloud registration, demonstrating performance that is on par or even superior to supervised oracles in large-scale real datasets.

Camera pose estimation

Deep image features like CAPS can be trained with relative pose labels generated by 5pt-RANSAC, bootstraped with the handcrafted SIFT feature. They can be later used in robust relative camera pose estimation.

Point cloud registration

Deep 3D features like FCGF can be trained with relative pose labels generated by 3pt-RANSAC, bootstraped by the handcrafted FPFH feature. They can be later used in robust point cloud registration.

Code

Please see code/ for detailed intructions about how to use the code base.

Citation

@inproceedings{yang2021sgp,
  title={Self-supervised Geometric Perception},
  author={Yang, Heng and Dong, Wei and Carlone, Luca and Koltun, Vladlen},
  booktitle={CVPR},
  year={2021}
}

theNded / SGP