  3D occupancy perception holds a pivotal role in recent vision-centric
autonomous driving systems by converting surround-view images into integrated
geometric and semantic representations within dense 3D grids. Nevertheless,
current models still encounter two main challenges: modeling depth accurately
in the 2D-3D view transformation stage, and overcoming the lack of
generalizability issues due to sparse LiDAR supervision. To address these
issues, this paper presents GEOcc, a Geometric-Enhanced Occupancy network
tailored for vision-only surround-view perception. Our approach is three-fold:
1) Integration of explicit lift-based depth prediction and implicit
projection-based transformers for depth modeling, enhancing the density and
robustness of view transformation. 2) Utilization of mask-based encoder-decoder
architecture for fine-grained semantic predictions; 3) Adoption of
context-aware self-training loss functions in the pertaining stage to
complement LiDAR supervision, involving the re-rendering of 2D depth maps from
3D occupancy features and leveraging image reconstruction loss to obtain denser
depth supervision besides sparse LiDAR ground-truths. Our approach achieves
State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image
resolution needed and the most weightless image backbone compared with current
models, marking an improvement of 3.3% due to our proposed contributions.
Comprehensive experimentation also demonstrates the consistent superiority of
our method over baselines and alternative approaches.
