ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction
This addresses safety-critical issues in autonomous driving by providing more calibrated occupancy estimates and reliable OOD detection, though it is incremental as it builds on existing methods with novel refinements.
The paper tackles the problem of 3D semantic occupancy prediction in autonomous driving, which is vulnerable to long-tailed class bias and out-of-distribution inputs, by proposing ProOOD, a lightweight plug-and-play method that achieves state-of-the-art performance, improving overall mIoU by +3.57% and tail-class mIoU by +24.80% on SemanticKITTI.
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.