Fast LiDAR Data Generation with Rectified Flows

Understand

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.