用流匹配提升激光雷达场景补全，解决训练与推理分布不一致问题。
LiFlow: Flow Matching for 3D LiDAR Scene Completion
- 采用最近邻流匹配损失，统一训练与推理的初始分布。
- 在S3DIS和ScanNet数据集上优于现有方法，全局覆盖与局部结构更优。
- 适合自动驾驶中处理遮挡和稀疏点云的场景补全任务。
在自动驾驶场景中，采集的激光雷达点云常因遮挡和远距离稀疏而受限，影响感知能力。场景补全方法可推断缺失部分。现有方法多采用局部点级去噪扩散模型，需预测高斯噪声，导致训练与推理时初始分布不一致。本文首次提出面向3D激光雷达场景补全的流匹配框架，通过近邻流匹配损失与切比雪夫距离损失，同时优化点云对齐的局部结构与全局覆盖。该方法在多个指标上达到当前最优性能。代码已开源：https://github.com/matteandre/LiFlow。
原文摘要 · Abstract (English)
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.
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