# R3D: Regional-guided Residual Radar Diffusion

arXiv: https://arxiv.org/abs/2601.06465 · HTML: https://mlsanity.com/paper/2601.06465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 10 Jan 2026.

## Builds on (most cited first, 25 of 25)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Resurrecting Recurrent Neural Networks for Long Sequences](https://mlsanity.com/paper/2303.06349/) (2023)
- [ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting](https://mlsanity.com/paper/2307.12348/) (2023)
- [PointMamba: A Simple State Space Model for Point Cloud Analysis](https://mlsanity.com/paper/2402.10739/) (2024)
- [Residual Denoising Diffusion Models](https://mlsanity.com/paper/2308.13712/) (2023)
- [Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud](https://mlsanity.com/paper/2207.11919/) (2022)
- [DocDiff: Document Enhancement via Residual Diffusion Models](https://mlsanity.com/paper/2305.03892/) (2023)
- [Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision](https://mlsanity.com/paper/2303.00462/) (2023)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise](https://mlsanity.com/paper/2311.14900/) (2023)
- [Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model](https://mlsanity.com/paper/2403.08460/) (2024)
- [Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data](https://mlsanity.com/paper/2404.06012/) (2024)
- [DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud](https://mlsanity.com/paper/2309.15374/) (2023)
- [Dynamic Attention-Guided Diffusion for Image Super-Resolution](https://mlsanity.com/paper/2308.07977/) (2023)
- [Neural Residual Diffusion Models for Deep Scalable Vision Generation](https://mlsanity.com/paper/2406.13215/) (2024)
- [R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model](https://mlsanity.com/paper/2503.17097/) (2025)
- [SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds](https://mlsanity.com/paper/2505.10601/) (2025)

## Built on by (most cited first, 2 of 2)

- [Multi-Adapter PPO: A Cross-Attention Enhanced Wavelength Selection Framework for LIBS Quantitative Analysis](https://mlsanity.com/paper/2606.17476/) (2026)
- [A Standard Processing Pipeline for High-accuracy Measurement of Few-shot Regression on Laser Induced Breakdown Spectroscopy](https://mlsanity.com/paper/2606.21960/) (2026)

## Abstract (arXiv)

Millimeter-wave radar enables robust environment perception in autonomous systems under adverse conditions yet suffers from sparse, noisy point clouds with low angular resolution. Existing diffusion-based radar enhancement methods either incur high learning complexity by modeling full LiDAR distributions or fail to prioritize critical structures due to uniform regional processing. To address these issues, we propose R3D, a regional-guided residual radar diffusion framework that integrates residual diffusion modeling-focusing on the concentrated LiDAR-radar residual encoding complementary high-frequency details to reduce learning difficulty-and sigma-adaptive regional guidance-leveraging radar-specific signal properties to generate attention maps and applying lightweight guidance only in low-noise stages to avoid gradient imbalance while refining key regions. Extensive experiments on the ColoRadar dataset demonstrate that R3D outperforms state-of-the-art methods, providing a practical solution for radar perception enhancement. Our anonymous code and pretrained models are released here: https://anonymous.4open.science/r/r3d-F836