# DART: Implicit Doppler Tomography for Radar Novel View Synthesis

arXiv: https://arxiv.org/abs/2403.03896 · HTML: https://mlsanity.com/paper/2403.03896/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 89 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 23 of 23)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [Neural LiDAR Fields for Novel View Synthesis](https://mlsanity.com/paper/2305.01643/) (2023)
- [Learning Neural Acoustic Fields](https://mlsanity.com/paper/2204.00628/) (2022)
- [NeRF2: Neural Radio-Frequency Radiance Fields](https://mlsanity.com/paper/2305.06118/) (2023)
- [Radar-Camera Pixel Depth Association for Depth Completion](https://mlsanity.com/paper/2106.02778/) (2021)
- [Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar](https://mlsanity.com/paper/1912.06613/) (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)
- [Pointillism: Accurate 3D bounding box estimation with multi-radars](https://mlsanity.com/paper/2203.04440/) (2022)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://mlsanity.com/paper/2204.01184/) (2022)
- [Neural Implicit Surface Reconstruction using Imaging Sonar](https://mlsanity.com/paper/2209.08221/) (2022)
- [Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar](https://mlsanity.com/paper/2306.09909/) (2023)
- [MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving](https://mlsanity.com/paper/2101.09293/) (2021)
- [DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar](https://mlsanity.com/paper/2109.09188/) (2021)

## Built on by (most cited first, 19 of 19)

- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion](https://mlsanity.com/paper/2504.11773/) (2025)
- [NeuRadar: Neural Radiance Fields for Automotive Radar Point Clouds](https://mlsanity.com/paper/2504.00859/) (2025)
- [Towards Foundational Models for Single-Chip Radar](https://mlsanity.com/paper/2509.12482/) (2025)
- [SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting](https://mlsanity.com/paper/2504.00159/) (2025)
- [4D-ROLLS: 4D Radar Occupancy Learning via LiDAR Supervision](https://mlsanity.com/paper/2505.13905/) (2025)
- [RF4D:Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes](https://mlsanity.com/paper/2505.20967/) (2025)
- [Controllable Radar Simulation with Waveform Parameter Embedding](https://mlsanity.com/paper/2506.03134/) (2025)
- [RISE: Single Static Radar-based Indoor Scene Understanding](https://mlsanity.com/paper/2511.14019/) (2025)
- [RadarGen: Automotive Radar Point Cloud Generation from Cameras](https://mlsanity.com/paper/2512.17897/) (2025)
- [RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields](https://mlsanity.com/paper/2605.26328/) (2026)
- [SH-SAS: An Implicit Neural Representation for Complex Spherical-Harmonic Scattering Fields for 3D Synthetic Aperture Sonar](https://mlsanity.com/paper/2509.11087/) (2025)
- [RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment](https://mlsanity.com/paper/2604.13492/) (2026)
- [mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis](https://mlsanity.com/paper/2608.28913/) (2026)
- [GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals](https://mlsanity.com/paper/2605.29097/) (2026)
- [Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals](https://mlsanity.com/paper/2605.29098/) (2026)
- [3D Point Splatting for mmWave Radar Novel View Synthesis](https://mlsanity.com/paper/2609.11894/) (2026)
- [DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes](https://mlsanity.com/paper/2609.39841/) (2026)

## Abstract (arXiv)

Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging, target detection, classification, and tracking. However, simulating realistic radar scans is a challenging task that requires an accurate model of the scene, radio frequency material properties, and a corresponding radar synthesis function. Rather than specifying these models explicitly, we propose DART - Doppler Aided Radar Tomography, a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. We then evaluate DART by constructing a custom data collection platform and collecting a novel radar dataset together with accurate position and instantaneous velocity measurements from lidar-based localization. In comparison to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets and additionally can be used to generate high quality tomographic images.