  Point cloud registration is the task of estimating the rigid transformation
that aligns a pair of point cloud fragments. We present an efficient and robust
framework for pairwise registration of real-world 3D scans, leveraging Hough
voting in the 6D transformation parameter space. First, deep geometric features
are extracted from a point cloud pair to compute putative correspondences. We
then construct a set of triplets of correspondences to cast votes on the 6D
Hough space, representing the transformation parameters in sparse tensors.
Next, a fully convolutional refinement module is applied to refine the noisy
votes. Finally, we identify the consensus among the correspondences from the
Hough space, which we use to predict our final transformation parameters. Our
method outperforms state-of-the-art methods on 3DMatch and 3DLoMatch benchmarks
while achieving comparable performance on KITTI odometry dataset. We further
demonstrate the generalizability of our approach by setting a new
state-of-the-art on ICL-NUIM dataset, where we integrate our module into a
multi-way registration pipeline.
