From One to Many: Dynamic Cross Attention Networks for LiDAR and Camera Fusion. (arXiv:2209.12254v1 [cs.CV])

LiDAR and cameras are two complementary sensors for 3D perception in
autonomous driving. LiDAR point clouds have accurate spatial and geometry
information, while RGB images provide textural and color data for context
reasoning. To exploit LiDAR and cameras jointly, existing fusion methods tend
to align each 3D point to only one projected image pixel based on calibration,
namely one-to-one mapping. However, the performance of these approaches highly
relies on the calibration quality, which is sensitive to the temporal and
spatial …

arxiv attention fusion lidar networks one to many