  Learning-based point cloud registration approaches have significantly
outperformed their traditional counterparts. However, they typically require
extensive training on specific datasets. In this paper, we propose , the first
zero-shot point cloud registration approach that eliminates the need for
training on point cloud datasets. The cornerstone of ZeroReg is the novel
transfer of image features from keypoints to the point cloud, enriched by
aggregating information from 3D geometric neighborhoods. Specifically, we
extract keypoints and features from 2D image pairs using a frozen pretrained 2D
backbone. These features are then projected in 3D, and patches are constructed
by searching for neighboring points. We integrate the geometric and visual
features of each point using our novel parameter-free geometric decoder.
Subsequently, the task of determining correspondences between point clouds is
formulated as an optimal transport problem. Extensive evaluations of ZeroReg
demonstrate its competitive performance against both traditional and
learning-based methods. On benchmarks such as 3DMatch, 3DLoMatch, and ScanNet,
ZeroReg achieves impressive Recall Ratios (RR) of over 84%, 46%, and 75%,
respectively.
