arXiv Machine Learning By Chong Liu, Luxuan Fu, Xuyu Feng, Zhen Dong, Bisheng Yang

WHU-Infra3D: A Full-stack Multi-modal Dataset and Benchmark for 3D Roadside Infrastructure Inventory

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arXiv:2606. 09882v1 Announce Type: cross Abstract: The paradigm of digital twin cities is shifting from coarse visual mapping toward more precise and actionable digitization of urban assets.

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arXiv Computer Vision
Sep 7

Out-of-Distribution Semantic Occupancy Prediction

The paper introduces Out-of-Distribution Semantic Occupancy Prediction, a task that focuses on detecting unknown objects in 3D voxel space for autonomous driving. It proposes Realistic Anomaly Augmentation to create two new datasets, VAA-KITTI and VAA-KITTI-360, and presents the OccOoD framework, which uses Cross‑Space Semantic Refinement to improve OoD detection while maintaining semantic occupancy accuracy. Experiments show OccOoD achieves an AuROC of 65.50% and an AuPRCr of 31.83% within a 1.2 m radius, demonstrating strong generalization to real‑world urban scenes.

By Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Ruiping Liu, Fei Teng, Kai Luo, Zhiyong Li, Kailun Yang