Learning Voxel-wise Observation Reliability for Robust LiDAR 3D Object Detection

Voxel-based 3D detectors achieve a favorable accuracy-efficiency trade-off, but remain vulnerable to weather- and sensor-induced corruptions. A key limitation is that existing voxel-based pipelines typically treat all occupied voxels as equally reliable, even though corruption can introduce spurious or perturbed observations with substantially different reliability. Once encoded, unreliable voxel features can be repeatedly propagated through sparse convolutions, contaminating otherwise reliable geometric representations. To address this issue, we propose VORL, a Voxel-wise Observation Reliability Learning framework for robust LiDAR 3D object detection. VORL estimates a continuous reliability score for each occupied voxel from its feature representation and lightweight observation cues, and introduces Reliability-aware Sparse Convolution (RaSC) to modulate source-voxel contributions before neighborhood aggregation. In this way, unreliable feature propagation is suppressed before feature mixing occurs, while reliable geometric evidence is largely preserved. Since voxel-wise reliability annotations are unavailable, we derive corruption-aware pseudo reliability targets from clean-to-corrupted correspondences available during corruption synthesis. Experiments on KITTI-C and nuScenes-C demonstrate consistent robustness improvements on diverse voxel-based detectors. On KITTI-C, VORL improves corrupted AP by 2.62-6.78 points across three baseline detectors while maintaining clean performance. More importantly, VORL can be seamlessly integrated into existing voxel-based detectors, while introducing only marginal computational and memory overhead.