  3D semantic occupancy prediction is a pivotal task in the field of autonomous
driving. Recent approaches have made great advances in 3D semantic occupancy
predictions on a single modality. However, multi-modal semantic occupancy
prediction approaches have encountered difficulties in dealing with the
modality heterogeneity, modality misalignment, and insufficient modality
interactions that arise during the fusion of different modalities data, which
may result in the loss of important geometric and semantic information. This
letter presents a novel multi-modal, i.e., LiDAR-camera 3D semantic occupancy
prediction framework, dubbed Co-Occ, which couples explicit LiDAR-camera
feature fusion with implicit volume rendering regularization. The key insight
is that volume rendering in the feature space can proficiently bridge the gap
between 3D LiDAR sweeps and 2D images while serving as a physical
regularization to enhance LiDAR-camera fused volumetric representation.
Specifically, we first propose a Geometric- and Semantic-aware Fusion
(GSFusion) module to explicitly enhance LiDAR features by incorporating
neighboring camera features through a K-nearest neighbors (KNN) search. Then,
we employ volume rendering to project the fused feature back to the image
planes for reconstructing color and depth maps. These maps are then supervised
by input images from the camera and depth estimations derived from LiDAR,
respectively. Extensive experiments on the popular nuScenes and SemanticKITTI
benchmarks verify the effectiveness of our Co-Occ for 3D semantic occupancy
prediction. The project page is available at
https://rorisis.github.io/Co-Occ_project-page/.
