Where Do the Gaussians Go? Foreground Attraction for Camera-Based Weakly Supervised 3D Occupancy
Camera-based 3D semantic occupancy models can be trained without 3D labels by rendering their predictions into the images and supervising them with 2D pseudo labels. In sparse Gaussian representations trained this way, Gaussian positions are learned only through pixel-wise rendering losses, which we hypothesize give small foreground objects little weight. In trained GaussianFlowOcc models, the Gaussian density on foreground voxels is on average only 1.62 times that on background voxels. We propose a training-only foreground attraction loss that back-projects foreground pseudo-label pixels with pseudo depth and pulls nearby Gaussians toward these 3D points, favoring Gaussians that already ...
| Authors | Jeagon Song, Jhonghyun An |
|---|---|
| Primary category | cs.CV |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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