ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction

• URL: http://arxiv.org/abs/2604.01081v1
• Date: Wed, 01 Apr 2026 16:11:59 GMT
• Title: ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction
• Abstract summary: 3D semantic occupancy prediction is central to autonomous driving.<n>Current methods are vulnerable to long-tailed class bias and out-of-distribution (OOD) inputs.<n>We present ProOOD, a lightweight, plug-and-play method that couples prototype-guided refinement with training-free OOD scoring.
• Score: 77.70461178466043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D semantic occupancy prediction is central to autonomous driving, yet current methods are vulnerable to long-tailed class bias and out-of-distribution (OOD) inputs, often overconfidently assigning anomalies to rare classes. We present ProOOD, a lightweight, plug-and-play method that couples prototype-guided refinement with training-free OOD scoring. ProOOD comprises (i) prototype-guided semantic imputation that fills occluded regions with class-consistent features, (ii) prototype-guided tail mining that strengthens rare-class representations to curb OOD absorption, and (iii) EchoOOD, which fuses local logit coherence with local and global prototype matching to produce reliable voxel-level OOD scores. Extensive experiments on five datasets demonstrate that ProOOD achieves state-of-the-art performance on both in-distribution 3D occupancy prediction and OOD detection. On SemanticKITTI, it surpasses baselines by +3.57% mIoU overall and +24.80% tail-class mIoU; on VAA-KITTI, it improves AuPRCr by +19.34 points, with consistent gains across benchmarks. These improvements yield more calibrated occupancy estimates and more reliable OOD detection in safety-critical urban driving. The source code is publicly available at https://github.com/7uHeng/ProOOD.

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