Global Consistent Point Cloud Registration Based on Lie-algebraic Cohomology
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Abstract (arXiv)
We present a novel, effective method for global point cloud registration problems by geometric topology. Based on many point cloud pairwise registration methods (e.g ICP), we focus on the problem of accumulated error for the composition of transformations along any loops. The major technical contribution of this paper is a linear method for the elimination of errors, using only solving a Poisson equation. We demonstrate the consistency of our method from Hodge-Helmhotz decomposition theorem and experiments on multiple RGBD datasets of real-world scenes. The experimental results also demonstrate that our global registration method runs quickly and provides accurate reconstructions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 47 references that are in the map. First posted on arXiv 15 Aug 2022.
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References · 4
- 2017A Survey of Structure from Motioncited by 158
- 2019IoU Loss for 2D/3D Object Detectioncited by 128
- 2019NeuRoRA: Neural Robust Rotation Averagingcited by 32
- 2018CELLO-3D: Estimating the Covariance of ICP in the Real Worldcited by 13
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