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October 20, 2025Open Access

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

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Authors

RWRui‐Xin WuHunan UniversityZLZihan LiHua Yuan Group (China)JWJin WangInstitute of High Energy Physics

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Implication

Novel method improves point cloud quality in autonomous driving by integrating range images and diffusion models.

Key Points

  • The proposed method significantly enhances the quality of mmWave radar point clouds, making them comparable to LiDAR outputs.
  • Extensive evaluations on public and self-constructed datasets confirm substantial performance improvements in point cloud density.
  • Utilizing range images with image diffusion models facilitates effective transfer of knowledge from pre-trained networks.
  • This approach addresses traditional mmWave radar limitations related to noise and sparse point clouds in harsh environments.

Cite This Study

Wu et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcce8d54a28a75cf1cf5https://doi.org/10.48550/arxiv.2503.02300
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data2024
  2. 2Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model2024
  3. 3Unsupervised Radar Point Cloud Enhancement via Arbitrary LiDAR Guided Diffusion Prior2025
  4. 4RadarSFD: Single-Frame Diffusion with Pretrained Priors for Radar Point Clouds2025
  5. 5Enhancing mmWave Radar Point Cloud via Visual-inertial Supervision2024