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arXiv · 2603.26759

Physics-Aware Diffusion for LiDAR Point Cloud Densification

Zeping Zhang·Robert Laganière
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Abstract

LiDAR perception is severely limited by the distance-dependent sparsity of distant objects. While diffusion models can recover dense geometry, they suffer from prohibitive latency and physical hallucinations manifesting as ghost points. We propose Scanline-Consistent Range-Aware Diffusion, a framework that treats densification as probabilistic refinement rather than generation. By leveraging Partial Diffusion (SDEdit) on a coarse prior, we achieve high-fidelity results in just 156ms. Our novel Ray-Consistency loss and Negative Ray Augmentation enforce sensor physics to suppress artifacts. Our method achieves state-of-the-art results on KITTI-360 and nuScenes, directly boosting off-the-shelf 3D detectors without retraining. Code will be made available.

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Zeping Zhang, Robert Laganière. 2026-03-23. Physics-Aware Diffusion for LiDAR Point Cloud Densification. https://arxiv.org/abs/2603.26759

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