R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model

2025-03-21

AI Summary

To address the low resolution and degraded downstream performance caused by sparsity in 4D radar point clouds, this paper proposes a joint framework integrating a Voxel-based Latent-space Diffusion Model (LVDM) and Latent Point Cloud Reconstruction (LPCR). Departing from conventional image-based representations, our method directly models cross-modal priors in the 3D voxel feature space, leveraging paired LiDAR point clouds as conditional guidance for radar point cloud super-resolution generation. Key innovations include: (i) the first application of diffusion modeling in a voxelized latent space for radar point cloud densification; and (ii) the design of the LPCR module to enable high-fidelity point cloud reconstruction. Evaluated on two benchmark datasets, the framework achieves a 6–10× increase in point cloud density, a 31.7% improvement in registration recall, and a 24.9% gain in object detection mAP.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or bird's eye view (BEV) images, we represent both LiDAR and 4D radar point clouds using voxel features, which more effectively capture 3D shape information. Subsequently, we propose the Latent Voxel Diffusion Model (LVDM), which performs the diffusion process in the latent space. Additionally, a novel Latent Point Cloud Reconstruction (LPCR) module is utilized to reconstruct point clouds from high-dimensional latent voxel features. As a result, R2LDM effectively generates LiDAR-like point clouds from paired raw radar data. We evaluate our approach on two different datasets, and the experimental results demonstrate that our model achieves 6- to 10-fold densification of radar point clouds, outperforming state-of-the-art baselines in 4D radar point cloud super-resolution. Furthermore, the enhanced radar point clouds generated by our method significantly improve downstream tasks, achieving up to 31.7% improvement in point cloud registration recall rate and 24.9% improvement in object detection accuracy.

Problem

• Generating dense 4D radar point clouds using diffusion models
• Super-resolving sparse radar data into LiDAR-like quality
• Improving downstream perception tasks through radar enhancement

Innovation

• Voxel features represent LiDAR and radar
• Latent Voxel Diffusion Model in latent space
• Latent Point Cloud Reconstruction from voxels