  Monocular 3D semantic occupancy prediction is becoming important in robot
vision due to the compactness of using a single RGB camera. However, existing
methods often do not adequately account for camera perspective geometry,
resulting in information imbalance along the depth range of the image. To
address this issue, we propose a vanishing point (VP) guided monocular 3D
semantic occupancy prediction framework named VPOcc. Our framework consists of
three novel modules utilizing VP. First, in the VPZoomer module, we initially
utilize VP in feature extraction to achieve information balanced feature
extraction across the scene by generating a zoom-in image based on VP. Second,
we perform perspective geometry-aware feature aggregation by sampling points
towards VP using a VP-guided cross-attention (VPCA) module. Finally, we create
an information-balanced feature volume by effectively fusing original and
zoom-in voxel feature volumes with a balanced feature volume fusion (BVFV)
module. Experiments demonstrate that our method achieves state-of-the-art
performance for both IoU and mIoU on SemanticKITTI and SSCBench-KITTI360. These
results are obtained by effectively addressing the information imbalance in
images through the utilization of VP. Our code will be available at
www.github.com/anonymous.
