Uncertainty-Aware Multi-Prototype Modeling and Peak-Preserving Calibration for Out-of-Distribution 3D Occupancy Prediction

Reliable out-of-distribution (OOD) 3D occupancy prediction requires distinguishing genuinely unfamiliar objects from rare but valid semantic patterns. However, long-tailed supervision and intra-class variation can leave rare known voxels weakly supported by the learned feature distribution, causing existing OOD detectors to conflate semantic rarity with distributional novelty and produce fragmented anomaly responses. To address this issue, we present UMPOcc, a plug-and-play framework for rare-vs-unknown disambiguation through complementary distributional and spatial reasoning. Its Uncertainty-Aware Multi-Prototype Occupancy Field (UMPOF) represents each semantic class with multiple modes characterized by empirical priors and feature dispersion, yielding more reliable source-domain support for rare known patterns. Peak-Preserving Spatial Calibration (PPSC) further exploits local occupancy support to reduce spatially inconsistent low-confidence responses while preserving high-confidence anomaly peaks. Extensive experiments across five benchmarks show strong occupancy and OOD performance, with consistent gains on tail classes and effective generalization to real-world scenes.