用修正流模型加速激光雷达数据生成，效率更高且质量不降。
Fast LiDAR Data Generation with Rectified Flows
- 采用修正流学习直线轨迹，减少采样步数
- 在KITTI-360数据集上实现快速高质量生成
- 适合需要实时生成的自动驾驶机器人应用
构建激光雷达生成模型有望作为强大的数据先验，用于自主移动机器人的复原、场景操作和可扩展仿真。近年来，基于扩散模型的方法涌现，显著提升了训练稳定性和生成质量。尽管取得成功，扩散模型需多次运行神经网络才能生成高质量样本，计算成本高，成为机器人应用的潜在障碍。为此，本文提出R2Flow，一种快速且高保真的激光雷达数据生成模型。该方法基于修正流，学习直线轨迹，相比扩散模型显著减少采样步骤。我们还设计了一种高效的Transformer架构，用于处理激光雷达距离与反射率图像表示。在KITTI-360数据集上的无条件生成实验表明，该方法在效率与质量方面均表现优异。
原文摘要 · Abstract (English)
Building LiDAR generative models holds promise as powerful data priors for restoration, scene manipulation, and scalable simulation in autonomous mobile robots. In recent years, approaches using diffusion models have emerged, significantly improving training stability and generation quality. Despite their success, diffusion models require numerous iterations of running neural networks to generate high-quality samples, making the increasing computational cost a potential barrier for robotics applications. To address this challenge, this paper presents R2Flow, a fast and high-fidelity generative model for LiDAR data. Our method is based on rectified flows that learn straight trajectories, simulating data generation with significantly fewer sampling steps compared to diffusion models. We also propose an efficient Transformer-based model architecture for processing the image representation of LiDAR range and reflectance measurements. Our experiments on unconditional LiDAR data generation using the KITTI-360 dataset demonstrate the effectiveness of our approach in terms of both efficiency and quality.
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