Jiaqi Yang, Shikun Zhang, Yanning Zhang, Xiyu Zhang
We lifted 3 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| zhangxy0517/3d-registration-with-maximal-cliques | canonical | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| integrate_trans | Not yet run | zhangxy0517/3d-registration-with-maximal-cliques/Python_implement/example_with_fpfh_fcgf.py code served (permissive licence) · get_code("718fc48733bdd79f") |
| rigid_transform_3d | Not yet run | zhangxy0517/3d-registration-with-maximal-cliques/Python_implement/example_with_fpfh_fcgf.py code served (permissive licence) · get_code("2d58ede8163c64d9") |
| transform | Not yet run | zhangxy0517/3d-registration-with-maximal-cliques/Python_implement/example_with_fpfh_fcgf.py code served (permissive licence) · get_code("9e72d23a822872a8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration method with maximal cliques (MAC). The key insight is to loosen the previous maximum clique constraint, and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each of which represents a consensus set. We perform node-guided clique selection then, where each node corresponds to the maximal clique with the greatest graph weight. 3) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration. Extensive experiments on U3M, 3DMatch, 3DLoMatch and KITTI demonstrate that MAC effectively increases registration accuracy, outperforms various state-of-the-art methods and boosts the performance of deep-learned methods. MAC combined with deep-learned methods achieves stateof-the-art registration recall of 95.7% / 78.9% on 3DMatch / 3DLoMatch.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.10854")
get_code_for_paper("2305.10854")
have("2305.10854")
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