Fast LiDAR Data Generation with Rectified Flows

2024-12-03

arXiv.org

AI Summary

Existing LiDAR diffusion models achieve high generation quality but rely on multi-step iterative sampling, resulting in slow inference and substantial computational overhead—unsuitable for real-time autonomous driving applications. To address this, we propose R2Flow, the first single-step (or minimally iterative) LiDAR generation framework leveraging Rectified Flows. R2Flow employs a lightweight Transformer to process image-like representations of LiDAR range and reflectance data, enabling unconditional, high-fidelity 3D point cloud synthesis. Evaluated on KITTI-360, R2Flow accelerates sampling by over 10× compared to state-of-the-art diffusion models while preserving—or even surpassing—their geometric fidelity and structural consistency. This breakthrough effectively resolves the long-standing efficiency–quality trade-off in LiDAR generative modeling, paving the way for real-time deployment in robotics and autonomous systems.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Develops fast LiDAR generative model for robotics applications
• Reduces computational cost in LiDAR data generation
• Improves efficiency and quality of LiDAR data simulation

Innovation

• R2Flow uses rectified flows for faster LiDAR generation.
• Transformer-based architecture processes LiDAR image data efficiently.
• Fewer sampling steps compared to traditional diffusion models.