DART: Implicit Doppler Tomography for Radar Novel View Synthesis
<|reference_start|>DART: Implicit Doppler Tomography for Radar Novel View Synthesis: 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.<|reference_end|>
@article{huang2024dart:,
    title={DART: Implicit Doppler Tomography for Radar Novel View Synthesis},
    author={Tianshu Huang, John Miller, Akarsh Prabhakara, Tao Jin, Tarana Laroia,
  Zico Kolter, Anthony Rowe},
    journal={arXiv preprint arXiv:2403.03896},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03896},
    primaryClass={cs.CV cs.LG}
}