解决3D占位预测中罕见类误判问题，提升自动驾驶安全性。
ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction
- 用原型引导填充遮挡区域，保持语义一致性
- 提升稀有类别表征，减少异常值误归类，尾部类mIoU提升24.80%
- 无需训练的异常检测，适用于实时安全场景
3D语义占位预测在自动驾驶中至关重要，但现有方法受长尾分布偏差和分布外（OOD）输入影响，常对异常样本过度自信地归入罕见类别。本文提出ProOOD，一种轻量级、即插即用的方法，结合原型引导精修与免训练的OOD评分。该方法包含：(i) 原型引导的语义补全，填补遮挡区域并保持类别一致性；(ii) 原型引导的尾部挖掘，强化稀有类别表征以抑制OOD吸收；(iii) EchoOOD，融合局部置信度一致性与局部/全局原型匹配，生成可靠的体素级OOD分数。在五个数据集上的实验表明，ProOOD在分布内3D占位预测与OOD检测上均达到当前最优性能：在SemanticKITTI上，总体mIoU提升3.57%，尾部类mIoU提升24.80%；在VAA-KITTI上，AuPRCr提升19.34点，各基准表现一致提升。结果使占位估计更校准，异常检测更可靠，适用于高危城市驾驶场景。代码已开源。
原文摘要 · Abstract (English)
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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