Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Di Wen, Danda Pani Paudel, Luc Van Gool, Kailun Yang

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.

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

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.

Paper Structure

This paper contains 22 sections, 15 equations, 6 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Preliminary
  5. Method Overview
  6. Class-wise Voxel Prototype Learning
  7. Prototype-Guided Refinement
  8. EchoOOD: Unsupervised Local & Global Prototype Scoring
  9. Experiments
  10. Experiment Setup
  11. Experimental Results
  12. Ablation Studies
  13. Conclusion
  14. Additional Qualitative Results
  15. Additional Quantitative Results
  16. ...and 7 more sections

Figures (6)

  • Figure 1: Overview of the proposed ProOOD framework. Coarse 3D features are derived from a 2D backbone and a view transformation module. These features are then refined by PGSI and PGTM modules, which leverage class-wise prototypes updated via EMA to enhance semantic completion and improve tail-class sensitivity. The resulting refined features are used to update the class prototypes, which in turn support both semantic occupancy prediction and OOD detection through the EchoOOD mechanism.
  • Figure 2: (a) Generation of Local and Global Voxel Prototypes.(b) EchoOOD. It computes OOD maps via three cues: local logit alignment, and local/global prototype matching.
  • Figure 3: Qualitative results of 3D occupancy prediction on the SemanticKITTI validation set behley2019semantickitti.
  • Figure 4: Qualitative results of out-of-distribution detection on the 07VAA sequence of VAA-KITTI zhang2025occood.
  • Figure 5: Failure case due to inaccurate occupancy prediction for OOD voxels.
  • ...and 1 more figures