Learning Quality-Conditioned Feature Displacements for LiDAR Out-of-Distribution Detection

LiDAR out-of-distribution (OOD) detection is essential for reliable open-world perception, where unexpected observations beyond the training distribution may appear during real-world deployment. Existing LiDAR OOD methods learn in-distribution feature structures without explicitly modeling the systematic representation changes induced by varying observation quality. Consequently, quality-induced representation deviations of in-distribution observations may be either incorrectly suppressed or absorbed as uncontrolled intra-class variation, obscuring the distinction between sensing-induced variation and genuine OOD deviations. To address this issue, we propose QuaD, a framework that learns quality-conditioned feature displacements to explicitly characterize admissible representation variation under different observation qualities. Our method establishes canonical class references from reliable high-quality observations and predicts class-dependent, quality-conditioned feature displacements to explain sensing-induced representation shifts. The predicted displacements are constrained during training such that quality-induced shifts can be accommodated while displacement-corrected representations remain consistent with the canonical class structure. At inference, semantic, contrastive, and displacement evidence are interpreted according to their class- and quality-conditioned in-distribution distributions, enabling more reliable distinction between admissible sensing-induced variation and genuine OOD deviations. Extensive experiments on multiple LiDAR OOD benchmarks demonstrate that QuaD consistently achieves state-of-the-art results, with up to a 108.3% improvement in point-level AP on SemanticKITTI-OoD over the previous best method.