  In this paper, we introduce PCR-CG: a novel 3D point cloud registration
module explicitly embedding the color signals into the geometry representation.
Different from previous methods that only use geometry representation, our
module is specifically designed to effectively correlate color into geometry
for the point cloud registration task. Our key contribution is a 2D-3D
cross-modality learning algorithm that embeds the deep 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
clouds. In this way, the overlapped 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 [25] as the baseline method and incorporate our proposed
module onto it. To validate the effectiveness of 2D features, we ablate
different 2D pre-trained networks and show a positive correlation between the
pre-trained weights and the task performance. Our experimental results indicate
a significant improvement of 6.5% registration recall over the baseline method
on the 3DLoMatch benchmark. We additionally evaluate our approach on SOTA
methods and observe consistent improvements, such as an improvement of 2.4%
registration recall over GeoTransformer as well as 3.5% over CoFiNet. Our study
reveals a significant advantages of correlating explicit deep color features to
the point cloud in the registration task.
