AI RESEARCH
ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction
arXiv CS.LG
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ArXi:2604.01081v1 Announce Type: cross 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