# Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model

arXiv: https://arxiv.org/abs/2403.08460 · HTML: https://mlsanity.com/paper/2403.08460/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 28 of 28)

- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [What Do Single-view 3D Reconstruction Networks Learn?](https://mlsanity.com/paper/1905.03678/) (2019)
- [Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models](https://mlsanity.com/paper/2308.01557/) (2023)
- [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud](https://mlsanity.com/paper/2207.11919/) (2022)
- [Distant Vehicle Detection Using Radar and Vision](https://mlsanity.com/paper/1901.10951/) (2019)
- [Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision](https://mlsanity.com/paper/2303.00462/) (2023)
- [See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar](https://mlsanity.com/paper/1911.00398/) (2019)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [ColoRadar: The Direct 3D Millimeter Wave Radar Dataset](https://mlsanity.com/paper/2103.04510/) (2021)
- [Diffusion Model with Perceptual Loss](https://mlsanity.com/paper/2401.00110/) (2023)
- [DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud](https://mlsanity.com/paper/2309.15374/) (2023)
- [Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar](https://mlsanity.com/paper/2311.08608/) (2023)
- [AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation](https://mlsanity.com/paper/2310.15050/) (2023)
- [RMap: Millimeter-Wave Radar Mapping Through Volumetric Upsampling](https://mlsanity.com/paper/2310.13188/) (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)
- [Towards Foundational Models for Single-Chip Radar](https://mlsanity.com/paper/2509.12482/) (2025)
- [Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images](https://mlsanity.com/paper/2503.02300/) (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)
- [RadarMP: Motion Perception for 4D mmWave Radar in Autonomous Driving](https://mlsanity.com/paper/2511.12117/) (2025)
- [R3D: Regional-guided Residual Radar Diffusion](https://mlsanity.com/paper/2601.06465/) (2026)
- [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)
- [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)
- [Radar2Shape: 3D Shape Reconstruction from High-Frequency Radar using Multiresolution Signed Distance Functions](https://mlsanity.com/paper/2511.17484/) (2025)
- [4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation](https://mlsanity.com/paper/2512.14235/) (2025)
- [RaUF: Learning the Spatial Uncertainty Field of Radar](https://mlsanity.com/paper/2603.01026/) (2026)
- [Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals](https://mlsanity.com/paper/2604.02603/) (2026)
- [GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation](https://mlsanity.com/paper/2609.10756/) (2026)

## Abstract (arXiv)

Millimeter wave (mmWave) radars have attracted significant attention from both academia and industry due to their capability to operate in extreme weather conditions. However, they face challenges in terms of sparsity and noise interference, which hinder their application in the field of micro aerial vehicle (MAV) autonomous navigation. To this end, this paper proposes a novel approach to dense and accurate mmWave radar point cloud construction via cross-modal learning. Specifically, we introduce diffusion models, which possess state-of-the-art performance in generative modeling, to predict LiDAR-like point clouds from paired raw radar data. We also incorporate the most recent diffusion model inference accelerating techniques to ensure that the proposed method can be implemented on MAVs with limited computing resources.We validate the proposed method through extensive benchmark comparisons and real-world experiments, demonstrating its superior performance and generalization ability. Code and pretrained models will be available at https://github.com/ZJU-FAST-Lab/Radar-Diffusion.