LetOccVote: Learning Weakly Supervised 3D Occupancy through Consensus

• URL: http://arxiv.org/abs/2609.04846v2
• Date: Mon, 07 Sep 2026 08:24:35 GMT
• Title: LetOccVote: Learning Weakly Supervised 3D Occupancy through Consensus
• Abstract summary: LetOccVote is a weakly supervised framework for 3D occupancy learning.<n>It is trained solely with 2D pseudo-label supervision without requiring 3D occupancy annotations.<n>It achieves 53.27 IoU and 20.39 mIoU, establishing state-of-the-art performance among methods with 2D pseudo-label supervision.
• Score: 20.730599565199935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 Vote exploits cross-frame geometric agreement to refine supported pseudo depth and reject contradictory estimates before volumetric lifting and depth supervision. For semantics, Semantic Vote aggregates pseudo-semantic observations in a shared 3D space to identify reliable and contested evidence, strengthening reliable semantic supervision while filtering unreliable pseudo-label segments. The entire framework is trained solely with 2D pseudo-label supervision without requiring 3D occupancy annotations. On Occ3D-nuScenes, LetOccVote achieves 53.27 IoU and 20.39 mIoU, establishing state-of-the-art performance among methods with 2D pseudo-label supervision.

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