VPOcc: Exploiting Vanishing Point for 3D Semantic Occupancy Prediction

• URL: http://arxiv.org/abs/2408.03551v2
• Date: Thu, 14 Aug 2025 04:31:02 GMT
• Title: VPOcc: Exploiting Vanishing Point for 3D Semantic Occupancy Prediction
• Authors: Junsu Kim, Junhee Lee, Ukcheol Shin, Jean Oh, Kyungdon Joo,
• Abstract summary: Understanding 3D scenes semantically and spatially is crucial for the safe navigation of robots and autonomous vehicles.<n>Camera-based 3D semantic occupancy prediction infers complete voxel grids from 2D images.<n>This task inherently suffers from a 2D-3D discrepancy, where objects of the same size in 3D space appear at different scales in a 2D image depending on their distance from the camera.<n>We propose a novel framework called VPOcc that leverages a vanishing point (VP) to mitigate the 2D-3D discrepancy at both the pixel and feature levels.
• Score: 24.947072696837118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding 3D scenes semantically and spatially is crucial for the safe navigation of robots and autonomous vehicles, aiding obstacle avoidance and accurate trajectory planning. Camera-based 3D semantic occupancy prediction, which infers complete voxel grids from 2D images, is gaining importance in robot vision for its resource efficiency compared to 3D sensors. However, this task inherently suffers from a 2D-3D discrepancy, where objects of the same size in 3D space appear at different scales in a 2D image depending on their distance from the camera due to perspective projection. To tackle this issue, we propose a novel framework called VPOcc that leverages a vanishing point (VP) to mitigate the 2D-3D discrepancy at both the pixel and feature levels. As a pixel-level solution, we introduce a VPZoomer module, which warps images by counteracting the perspective effect using a VP-based homography transformation. In addition, as a feature-level solution, we propose a VP-guided cross-attention (VPCA) module that performs perspective-aware feature aggregation, utilizing 2D image features that are more suitable for 3D space. Lastly, we integrate two feature volumes extracted from the original and warped images to compensate for each other through a spatial volume fusion (SVF) module. By effectively incorporating VP into the network, our framework achieves improvements in both IoU and mIoU metrics on SemanticKITTI and SSCBench-KITTI360 datasets. Additional details are available at https://vision3d-lab.github.io/vpocc/.

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