用物理约束扩散模型提升激光雷达点云密度，速度快且无伪点。
Physics-Aware Diffusion for LiDAR Point Cloud Densification
- 将点云补全视为概率优化而非生成，结合粗略先验进行精修。
- 156毫秒完成补全，KITTI-360和nuScenes上效果领先。
- 引入射线一致性损失与负向射线增强，抑制物理错误的伪点。
激光雷达感知受远距离物体稀疏性的严重限制。尽管扩散模型可恢复密集几何结构，但存在计算延迟高和产生物理上不合理的伪点（鬼点）的问题。本文提出扫描线一致的范围感知扩散框架，将点云补全视为概率性精修而非生成过程。通过在粗略先验上应用部分扩散（SDEdit），仅需156毫秒即可实现高保真结果。我们设计的射线一致性损失与负向射线增强机制，有效利用传感器物理特性抑制伪点。该方法在KITTI-360和nuScenes数据集上达到当前最优性能，无需重新训练即可显著提升现成3D检测器的性能。代码将公开。
原文摘要 · Abstract (English)
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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