# Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data

arXiv: https://arxiv.org/abs/2404.06012 · HTML: https://mlsanity.com/paper/2404.06012/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Symbolic Discovery of Optimization Algorithms](https://mlsanity.com/paper/2302.06675/) (2023)
- [MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation](https://mlsanity.com/paper/2205.09853/) (2022)
- [Image Restoration with Mean-Reverting Stochastic Differential Equations](https://mlsanity.com/paper/2301.11699/) (2023)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud](https://mlsanity.com/paper/2207.11919/) (2022)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [Deep Radar Detector](https://mlsanity.com/paper/1906.12187/) (2019)
- [Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM](https://mlsanity.com/paper/2309.08086/) (2023)
- [RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving](https://mlsanity.com/paper/2303.18084/) (2023)

## Built on by (most cited first, 17 of 17)

- [R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model](https://mlsanity.com/paper/2503.17097/) (2025)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images](https://mlsanity.com/paper/2503.02300/) (2025)
- [mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstruction](https://mlsanity.com/paper/2503.02375/) (2025)
- [HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds](https://mlsanity.com/paper/2602.11554/) (2026)
- [Unsupervised Radar Point Cloud Enhancement via Arbitrary LiDAR Guided Diffusion Prior](https://mlsanity.com/paper/2505.09887/) (2025)
- [R3D: Regional-guided Residual Radar Diffusion](https://mlsanity.com/paper/2601.06465/) (2026)
- [GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution](https://mlsanity.com/paper/2506.07897/) (2025)
- [RaLD: Generating High-Resolution 3D Radar Point Clouds with Latent Diffusion](https://mlsanity.com/paper/2511.07067/) (2025)
- [REXO: Indoor Multi-View Radar Object Detection via 3D Bounding Box Diffusion](https://mlsanity.com/paper/2511.17806/) (2025)
- [Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets](https://mlsanity.com/paper/2603.14507/) (2026)
- [MSDNet: Efficient 4D Radar Super-Resolution via Multi-Stage Distillation](https://mlsanity.com/paper/2509.13149/) (2025)
- [Wave-Former: Through-Occlusion 3D Reconstruction via Wireless Shape Completion](https://mlsanity.com/paper/2511.14152/) (2025)
- [Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments](https://mlsanity.com/paper/2511.16091/) (2025)
- [4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation](https://mlsanity.com/paper/2512.14235/) (2025)
- [SDCM: Simulated Densifying and Compensatory Modeling Fusion for Radar-Vision 3-D Object Detection in Internet of Vehicles](https://mlsanity.com/paper/2602.00149/) (2026)
- [GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation](https://mlsanity.com/paper/2609.10756/) (2026)

## Abstract (arXiv)

The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar point clouds are relatively sparse and contain massive ghost points, which greatly limits the development of mmWave radar technology. In this paper, we propose a novel point cloud super-resolution approach for 3D mmWave radar data, named Radar-diffusion. Our approach employs the diffusion model defined by mean-reverting stochastic differential equations(SDE). Using our proposed new objective function with supervision from corresponding LiDAR point clouds, our approach efficiently handles radar ghost points and enhances the sparse mmWave radar point clouds to dense LiDAR-like point clouds. We evaluate our approach on two different datasets, and the experimental results show that our method outperforms the state-of-the-art baseline methods in 3D radar super-resolution tasks. Furthermore, we demonstrate that our enhanced radar point cloud is capable of downstream radar point-based registration tasks.