Overlap-Guided Coarse-to-Fine Correspondence Prediction for Point Cloud Registration
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, 2022-July, pp. 1-6
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Overlap-Guided Coarse-to-Fine.pdf | Accepted version | 574.44 kB |
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Establishing reliable correspondences between a pair of point clouds is essential for registration with partial overlaps. However, existing correspondence estimation works usually struggle to distinguish the points in overlap and non-overlap regions. This paper thus proposes an Overlap-guided Coarse-to-Fine Network, named OCFNet, which first establishes correspondences at a coarse level and then refines them at a point level. Specifically, at the coarse level, our model first aggregates two point clouds into smaller sets of super-points with associated features and overlap scores, followed by establishing coarse-level correspondences between the two sets of super-points under the guidance of overlap scores. On the fine stage, a decoder recovers the raw points while jointly learning the associated features and overlap scores. Coarse-level proposals are then expanded to patches, and point-level correspondences are sequentially refined from the corresponding patches. We conducted comprehensive experiments on 3DMatch, 3DLoMatch, and KITTI benchmarks to show the effectiveness of the proposed method. [code]
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