ProOOD：原型引导的分布外3D占用预测
ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction
面向自动驾驶3D占用预测的免训练OOD打分（局部logit一致性与局部/全局原型匹配融合）与原型引导的尾部类挖掘，可直接迁移到其他密集预测任务的异常检测与长尾校准中。
原文依据 EchoOOD, which fuses local logit coherence with local and global prototype matching to produce reliable voxel-level OOD scores
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论3D语义占用预测是自动驾驶的核心环节，然而现有方法易受长尾类别偏置与分布外（OOD）输入的影响，常以过高置信度将异常分配给稀有类别。本文提出ProOOD，一种轻量、即插即用的方法，将原型引导的细化与免训练的OOD打分相结合。ProOOD包含三部分：(i) 原型引导的语义填补，用类别一致的特征填充被遮挡区域；(ii) 原型引导的尾部挖掘，增强稀有类别的表征以抑制OOD吸收；(iii) EchoOOD，融合局部logit一致性与局部及全局原型匹配，产生可靠的体素级OOD分数。在五个数据集上的大量实验表明，ProOOD在分布内3D占用预测与OOD检测两方面均达到最优性能。在SemanticKITTI上，其整体平均交并比超越基线+3.57%，尾部类别平均交并比提升+24.80%；在VAA-KITTI上，AuPRC_r提升+19.34个点，并在各基准上取得一致增益。这些改进带来了更校准的占用估计与更可靠的OOD检测，有利于安全攸关的城市驾驶。源代码将公开。
英文原文摘要 1240 字符
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 AuPRC_r 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 will be made publicly available.