SDFReg: Learning Signed Distance Functions for Point Cloud Registration
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Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy and partial point clouds. To this end, we propose a novel framework for noisy and partial point cloud registration. By introducing a neural implicit function representation, we replace the problem of rigid registration between point clouds with a registration problem between the point cloud and the neural implicit function. We then alternately optimize the implicit function representation and the registration between the implicit function and point cloud. In this way, point cloud registration can be performed in a coarse-to-fine manner. Since our method avoids computing point correspondences, it is robust to the noise and incompleteness of point clouds. Compared with the registration methods based on global features, our method can deal with surfaces with large density variations and achieve higher registration accuracy. Experimental results and comparisons demonstrate the effectiveness of the proposed framework.
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