Better match 3D point clouds having partial overlap using Gaussian Mixture Model with OGMM
Better match 3D point clouds having partial overlap using Gaussian Mixture Model with OGMM
Overlap-guided Gaussian Mixture Models for Point Cloud Registration
arXiv paper abstract https://arxiv.org/abs/2210.09836v1
arXiv PDF paper https://arxiv.org/pdf/2210.09836v1.pdf
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.
However, registering point cloud pairs in the case of partial overlap is still a challenge.
... proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters.
... reformulate the registration problem as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized.
... introduce a Transformer-based detection module to detect overlapping regions, and represent the input point clouds using GMMs by guiding their alignment through overlap scores computed by this detection module.
... method achieves superior registration accuracy and efficiency than state-of-the-art methods when handling point clouds with partial overlap and different densities on synthetic and real-world datasets ...
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