  In self-driving applications, LiDAR data provides accurate information about
distances in 3D but lacks the semantic richness of camera data. Therefore,
state-of-the-art methods for perception in urban scenes fuse data from both
sensor types. In this work, we introduce a novel self-supervised method to fuse
LiDAR and camera data for self-driving applications. We build upon masked
autoencoders (MAEs) and train deep learning models to reconstruct masked LiDAR
data from fused LiDAR and camera features. In contrast to related methods that
use birds-eye-view representations, we fuse features from dense spherical LiDAR
projections and features from fish-eye camera crops with a similar field of
view. Therefore, we reduce the learned spatial transformations to moderate
perspective transformations and do not require additional modules to generate
dense LiDAR representations. Code is available at:
https://github.com/KIT-MRT/masked-fusion-360
