[2604.09232] Neural Distribution Prior for LiDAR Out-of-Distribution Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.09232 (cs) [Submitted on 10 Apr 2026] Title:Neural Distribution Prior for LiDAR Out-of-Distribution Detection Authors:Zizhao Li, Zhengkang Xiang, Jiayang Ao, Feng Liu, Joseph West, Kourosh Khoshelham View a PDF of the paper titled Neural Distribution Prior for LiDAR Out-of-Distribution Detection, by Zizhao Li and 5 other authors View PDF HTML (experimental) Abstract:LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operate under the closed-set assumption and often fail to recognize unexpected out-of-distribution (OOD) objects in the open world. Existing OOD scoring functions exhibit limited performance because they ignore the pronounced class imbalance inherent in LiDAR OOD detection and assume a uniform class distribution. To address this limitation, we propose the Neural Distribution Prior (NDP), a framework that models the distributional structure of network predictions and adaptively reweights OOD scores based on alignment with a learned distribution prior. NDP dynamically captures the logit distribution patterns of training data and corrects class-dependent confidence bias through an attention-based module. We further introduce a Perlin noise-based OOD synthesis strategy that generates diverse auxiliary OOD samples from input scans, enabling robust OOD training without external datasets. Extensive experimen...