Feature-Metric Registration: A Fast Semi-Supervised Approach for Robust Point Cloud Registration Without Correspondences

By Xiaoshui Huang, Guofeng Mei. Computer Vision and Pattern Recognition, 2020

318 citations recorded by the source corpus.

We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences. The advantage of the feature-metric projection error is robust to noise, outliers and density difference in contrast to the geometric projection error. Besides, minimising the feature-metric projection error does not need to search the correspondences so that the optimisation speed is fast. The principle behind the proposed method is that the feature difference is smallest if point clouds are aligned very well. We train the proposed method in a semi-supervised or unsupervised approach, which requires limited or no registration label data. Experiments demonstrate our method obtains higher accuracy and robustness than the state-of-the-art methods. Besides, experimental results show that the proposed method can handle significant noise and density difference, and solve both same-source and cross-source point cloud registration.

Subfields: Aerospace Engineering, Computational Mechanics, Geology

View Paper: https://www.semanticscholar.org/paper/8b3943382089e430dff37775b6b1f792d81c58be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Xiaoshui Huang
2. Guofeng Mei
3. Rana Abbas
4. Jian Zhang
5. Fu Li
6. Benjamin Busam
7. Mahdi Saleh
8. Slobodan Ilic

In Computational Mechanics

1. Xiaoshui Huang
2. Guofeng Mei
3. Rana Abbas
4. Jian Zhang
5. Fu Li
6. Benjamin Busam
7. Mahdi Saleh
8. Slobodan Ilic

In Geology

1. Xiaoshui Huang
2. Guofeng Mei
3. Rana Abbas
4. Jian Zhang
5. Fu Li
6. Benjamin Busam
7. Mahdi Saleh
8. Slobodan Ilic

Citations in the corpus

• A comprehensive survey on point cloud registration (2021, methodology): “Based on our knowledge, FMR [40] is the first learning-based method to solve the cross-source point cloud registration.”
• Geometric Transformer for Fast and Robust Point Cloud Registration (2022, background): “5(bottom): FMR [17], DGR [7] and HRegNet [22].”
• CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration (2021, methodology): “The second [17, 18] includes the correspondence-free methods based on the intuition that the feature distance between two well-aligned point clouds should be small.”