PCR-CG: Point Cloud Registration via Deep Explicit Color and Geometry.

In this paper, we introduce PCR-CG: a novel 3D point cloud registration module explicitly embedding the color signals into geometry representation. Different from the previous SOTA methods that used only geometry representation, our module is specifically designed to effectively correlate color and geometry for the point cloud registration task. Our key contribution is a 2D-3D cross-modality learning algorithm that embeds the features learned from color signals to the geometry representation. With our designed 2D-3D projection module, the pixel features in a square region centered at correspondences perceived from images are effectively correlated with point cloud representations. In this way, the overlap regions can be inferred not only from point cloud but also from the texture appearances. Adding color is non-trivial. We compare against a variety of baselines designed for adding color to 3D, such as exhaustively adding per-pixel features or RGB values in an implicit manner. We leverage Predator as our baseline method and incorporate our module into it. Our experimental results indicate a significant improvement on the 3DLoMatch benchmark. With the help of our module, we achieve a significant improvement of \(6.5\%\) registration recall with 5000 sampled points over our baseline method. To validate the effectiveness of 2D features on 3D, we ablate different 2D pre-trained networks and show a positive correlation between the pre-trained weights and task performance. Our study reveals a significant advantage of correlating explicit deep color features to the point cloud in the registration task.