  Image and Point Clouds provide different information for robots. Finding the
correspondences between data from different sensors is crucial for various
tasks such as localization, mapping, and navigation. Learning-based descriptors
have been developed for single sensors; there is little work on cross-modal
features. This work treats learning cross-modal features as a dense contrastive
learning problem. We propose a Tuple-Circle loss function for cross-modality
feature learning. Furthermore, to learn good features and not lose generality,
we developed a variant of widely used PointNet++ architecture for point cloud
and U-Net CNN architecture for images. Moreover, we conduct experiments on a
real-world dataset to show the effectiveness of our loss function and network
structure. We show that our models indeed learn information from both images as
well as LiDAR by visualizing the features.
