DART: Implicit Doppler Tomography for Radar Novel View Synthesis

Tianshu Huang, John Miller, Akarsh Prabhakara, Tao Jin, Tarana Laroia, Zico Kolter, Anthony Rowe; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 24118-24129

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.