The Sparse ICP method formulates the problem using sparsity-inducing norms, significantly improving the resilience of the registration process to large amounts of noise and outliers, but introduces a significant performance degradation.
This study proposes a point cloud registration algorithm based on partial optimal transport via a hard marginal constraint that achieves state‐of‐the‐art ...
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With a convergence guarantee, ICP computes a locally optimal registration by alternately solving for closest corre- spondences and optimal rigid alignment.
With a convergence guarantee, ICP computes a locally optimal registration by alternately solving for closest corre- spondences and optimal rigid alignment.
The Sparse ICP method formulates the problem using sparsity-inducing norms, significantly improving the resilience of the registration process to large amounts ...
The Sparse ICP method formulates the problem using sparsity-inducing norms, significantly improving the resilience of the registration process to large amounts ...
We have introduced a new sampling method that helps convergence for scenes with small, sparse features. Finally, we have presented an optimized. ICP algorithm ...
Jan 26, 2021 · Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a ...
A Fast Global Optimal Strategy for Iteration Closest Point Using 2D ...
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Dec 14, 2023 · 4.3. Sparse Sampling. According to the analysis in Section 4.2, this algorithm needs to calls to BnB and ICP to process the point-set repeatedly ...
This repository contains an implementation of the Sparse Iterative Closest Point. It was implemented for the course Nuage de Point et Modélisation at Master MVA ...
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