LetOccVote: Learning Weakly Supervised 3D Occupancy through Consensus
Weakly supervised 3D occupancy prediction reduces the reliance on costly 3D annotations by learning from 2D pseudo-labels generated by vision foundation models. However, existing methods typically use these imperfect pseudo-labels directly as supervision, making occupancy learning vulnerable to erroneous geometric and semantic targets. We observe that agreement across repeated observations provides an inexpensive and reliable cue for assessing pseudo-label reliability. Based on this observation, we propose \textbf{LetOccVote}, a weakly supervised Gaussian-based occupancy framework that leverages cross-frame voting to improve both geometric and semantic supervision. For geometry, Depth Vot...
| Authors | Chi Zhang, Qi Song, Feifei Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 September 2026 |
| Last updated at source | 7 September 2026 |
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