In autonomous driving scenarios, the collected LiDAR point clouds can be challenged by occlusion and long-range sparsity, limiting the perception of autonomous driving systems. Scene completion methods can infer the missing parts of incomplete 3D LiDAR scenes. Recent methods adopt local point-level denoising diffusion probabilistic models, which require predicting Gaussian noise, leading to a mismatch between training and inference initial distributions. This paper introduces the first flow matching framework for 3D LiDAR scene completion, improving upon diffusion-based methods by ensuring consistent initial distributions between training and inference. The model employs a nearest neighbor flow matching loss and a Chamfer distance loss to enhance both local structure and global coverage in the alignment of point clouds. LiFlow achieves state-of-the-art performance across multiple metrics. Code: https://github.com/matteandre/LiFlow.

翻译：在自动驾驶场景中，采集的激光雷达点云常受遮挡与远距离稀疏性的影响，制约了自动驾驶系统的感知能力。场景补全方法能够推断不完整三维激光雷达场景的缺失部分。现有方法多采用局部点级去噪扩散概率模型，其需预测高斯噪声，导致训练与推理的初始分布不匹配。本文首次提出面向三维激光雷达场景补全的流匹配框架，通过确保训练与推理间初始分布的一致性，改进了基于扩散的方法。该模型采用最近邻流匹配损失与Chamfer距离损失，以增强点云对齐中的局部结构与全局覆盖能力。LiFlow在多项指标上均达到了最先进的性能。代码：https://github.com/matteandre/LiFlow。