Regulating Intermediate 3D Features for Vision-Centric Autonomous
  Driving
Junkai Xu Liang Peng Haoran Cheng Linxuan Xia Qi Zhou Dan Deng Wei Qian Wenxiao Wang Deng Cai
Multi-camera perception tasks have gained significant attention in the field
of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot
(LSS) in the multi-camera setting cannot produce suitable dense 3D features due
to the projection nature and uncontrollable densification process. To resolve
this problem, we propose to regulate intermediate dense 3D features with the
help of volume rendering. Specifically, we employ volume rendering to process
the dense 3D features to obtain corresponding 2D features (e.g., depth maps,
semantic maps), which are supervised by associated labels in the training. This
manner regulates the generation of dense 3D features on the feature level,
providing appropriate dense and unified features for multiple perception tasks.
Therefore, our approach is termed Vampire, stands for "Volume rendering As
Multi-camera Perception Intermediate feature REgulator". Experimental results
on the Occ3D and nuScenes datasets demonstrate that Vampire facilitates
fine-grained and appropriate extraction of dense 3D features, and is
competitive with existing SOTA methods across diverse downstream perception
tasks like 3D occupancy prediction, LiDAR segmentation and 3D objection
detection, while utilizing moderate GPU resources. We provide a video
demonstration in the supplementary materials and Codes are available at
github.com/cskkxjk/Vampire.