CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration

By Fu Li, Mahdi Saleh, Benjamin Busam, Slobodan Ilic. Neural Information Processing Systems, 2021

338 citations recorded by the source corpus.

We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoints detected from dense points but usually struggle to guarantee their repeatability. To address this issue, we present CoFiNet - Coarse-to-Fine Network which extracts hierarchical correspondences from coarse to fine without keypoint detection. On a coarse scale and guided by a weighting scheme, our model firstly learns to match down-sampled nodes whose vicinity points share more overlap, which significantly shrinks the search space of a consecutive stage. On a finer scale, node proposals are consecutively expanded to patches that consist of groups of points together with associated descriptors. Point correspondences are then refined from the overlap areas of corresponding patches, by a density-adaptive matching module capable to deal with varying point density. Extensive evaluation of CoFiNet on both indoor and outdoor standard benchmarks shows our superiority over existing methods. Especially on 3DLoMatch where point clouds share less overlap, CoFiNet significantly outperforms state-of-the-art approaches by at least 5% on Registration Recall, with at most two-third of their parameters.

Subfields: Aerospace Engineering, Computational Mechanics, Geology

View Paper: https://www.semanticscholar.org/paper/bf030afd821520593ee553a194485220b2151ed5

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. Zheng Qin
2. Changjian Wang
3. Kaiping Xu
4. Yulan Guo
5. Yuxing Peng

In Computational Mechanics

1. Zheng Qin
2. Changjian Wang
3. Kaiping Xu
4. Yulan Guo
5. Yuxing Peng

In Geology

1. Zheng Qin
2. Changjian Wang
3. Kaiping Xu
4. Yulan Guo
5. Yuxing Peng

Citations in the corpus

• Geometric Transformer for Fast and Robust Point Cloud Registration (2022, methodology): “Inspired by the recent advances in image matching [22, 25, 39], keypoint-free methods [36] downsample the input point clouds into superpoints and then match them through examining whether their local neighborhood (patch) overlaps.”