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Large-scale Point Cloud Registration Based on Graph Matching Optimization
Point Clouds Registration is a fundamental and challenging problem in 3D
computer vision. It has been shown that the isometric transformation is an
essential property in rigid point cloud registration, but the existing methods
only utilize it in the outlier rejection stage. In this paper, we emphasize
that the isometric transformation is also important in the feature learning
stage for improving registration quality. We propose a \underline{G}raph
\underline{M}atching \underline{O}ptimization based \underline{Net}work
(denoted as GMONet for short), which utilizes the graph matching method to
explicitly exert the isometry preserving constraints in the point feature
learning stage to improve %refine the point representation. Specifically, we
%use exploit the partial graph matching constraint to enhance the overlap
region detection abilities of super points ( down-sampled key points)
and full graph matching to refine the registration accuracy at the fine-level
overlap region. Meanwhile, we leverage the mini-batch sampling to improve the
efficiency of the full graph matching optimization. Given high discriminative
point features in the evaluation stage, we utilize the RANSAC approach to
estimate the transformation between the scanned pairs. The proposed method has
been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The
experimental results show that our method achieves competitive performance
compared with the existing state-of-the-art baselines
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