Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images

2025-03-04

AI Summary

Millimeter-wave radar point clouds suffer from severe sparsity and noise, limiting their utility in robotics and autonomous driving. Existing diffusion-based super-resolution methods—relying on range-angle-height (RAH) or bird’s-eye-view (BEV) representations—exhibit poor adaptability and low reconstruction fidelity. To address this, we propose the first range-image (RI)-driven diffusion model transfer paradigm: radar point clouds are projected onto range images, enhanced via pre-trained image diffusion models (e.g., Stable Diffusion), and then reconstructed into dense 3D point clouds through radar–image joint feature alignment and 3D inverse mapping. This approach overcomes modality-specific representation bottlenecks and enables effective cross-modal knowledge transfer. Evaluated on both public and in-house datasets, our method achieves state-of-the-art performance, significantly improving point cloud density, geometric accuracy, and structural integrity—yielding LiDAR-like 3D point clouds.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird's eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar.

Problem

• Enhance sparse, noisy mmWave radar point clouds.
• Overcome limitations of traditional RAH/BEV representations.
• Generate dense, LiDAR-like 3D point clouds.

Innovation

• Fuses range images with diffusion models
• Enhances mmWave radar to LiDAR-like quality
• Uses pre-trained models for effective knowledge transfer