让激光雷达生成更真实，修复2D图像的3D几何错误
L3DR: 3D-aware LiDAR Diffusion and Rectification
- 在3D空间中预测点云偏移，修复2D生成的深度渗漏和波浪表面
- 在KITTI、nuScenes等4个数据集上实现最佳几何真实感表现
- 可适配多种激光雷达扩散模型，计算开销小，适合工程落地
基于范围视图（RV）的激光雷达扩散模型虽在2D逼真度上取得进展，但忽视3D几何真实性，常产生深度渗漏和波浪表面等RV伪影。本文提出L3DR，一种3D感知的激光雷达扩散与校正框架，可在3D空间中回归并消除RV伪影，精准恢复局部几何结构。理论与实证分析表明，3D模型在生成清晰真实边界方面天然优于2D模型。据此设计3D残差回归网络，在3D空间中预测点级偏移以校正伪影；同时引入Welsch损失，聚焦局部几何，抑制异常区域干扰。在KITTI、KITTI360、nuScenes和Waymo等多个基准上实验显示，L3DR持续实现最优生成质量与卓越几何真实性。此外，L3DR对不同激光雷达扩散模型具有通用性，计算开销极低。
原文摘要 · Abstract (English)
Range-view (RV) based LiDAR diffusion has recently made huge strides towards 2D photo-realism. However, it neglects 3D geometry realism and often generates various RV artifacts such as depth bleeding and wavy surfaces. We design L3DR, a 3D-aware LiDAR Diffusion and Rectification framework that can regress and cancel RV artifacts in 3D space and restore local geometry accurately. Our theoretical and empirical analysis reveals that 3D models are inherently superior to 2D models in generating sharp and authentic boundaries. Leveraging such analysis, we design a 3D residual regression network that rectifies RV artifacts and achieves superb geometry realism by predicting point-level offsets in 3D space. On top of that, we design a Welsch Loss that helps focus on local geometry and ignore anomalous regions effectively. Extensive experiments over multiple benchmarks including KITTI, KITTI360, nuScenes and Waymo show that the proposed L3DR achieves state-of-the-art generation and superior geometry-realism consistently. In addition, L3DR is generally applicable to different LiDAR diffusion models with little computational overhead.
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