Out-of-Distribution Semantic Occupancy Prediction

• URL: http://arxiv.org/abs/2506.21185v1
• Date: Thu, 26 Jun 2025 12:44:29 GMT
• Title: Out-of-Distribution Semantic Occupancy Prediction
• Authors: Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Ruiping Liu, Fei Teng, Kai Luo, Zhiyong Li, Kailun Yang,
• Abstract summary: We introduce Out-of-Distribution Semantic Occupancy Prediction, targeting OoD detection in 3D voxel space.<n>We introduce OccOoD, a novel framework integrating OoD detection into 3D semantic occupancy prediction, with Voxel-BEV Progressive Fusion (VBPF)<n> Experimental results demonstrate that OccOoD achieves state-of-the-art OoD detection with an AuROC of 67.34% and an AuPRCr of 29.21% within a 1.2m region, while maintaining competitive occupancy prediction performance.
• Score: 26.178096447889605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Semantic Occupancy Prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, existing methods focus on in-distribution scenes, making them susceptible to Out-of-Distribution (OoD) objects and long-tail distributions, which increases the risk of undetected anomalies and misinterpretations, posing safety hazards. To address these challenges, we introduce Out-of-Distribution Semantic Occupancy Prediction, targeting OoD detection in 3D voxel space. To fill the gaps in the dataset, we propose a Synthetic Anomaly Integration Pipeline that injects synthetic anomalies while preserving realistic spatial and occlusion patterns, enabling the creation of two datasets: VAA-KITTI and VAA-KITTI-360. We introduce OccOoD, a novel framework integrating OoD detection into 3D semantic occupancy prediction, with Voxel-BEV Progressive Fusion (VBPF) leveraging an RWKV-based branch to enhance OoD detection via geometry-semantic fusion. Experimental results demonstrate that OccOoD achieves state-of-the-art OoD detection with an AuROC of 67.34% and an AuPRCr of 29.21% within a 1.2m region, while maintaining competitive occupancy prediction performance. The established datasets and source code will be made publicly available at https://github.com/7uHeng/OccOoD.

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