DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes
Reconstructing dynamic driving scenes from recorded sensor data supports closed-loop evaluation of autonomous driving systems by synthesizing observations beyond the original trajectory. Unlike cameras and LiDAR, radar measures radial velocity directly through Doppler. Yet existing radar novel-view synthesis fails to exploit this capability: methods addressing dynamic scenes reconstruct only range-azimuth tensors, while methods that render Doppler assume static scenes. Moreover, because radar processing spreads each reflection across multiple bins, existing representations absorb this spread into scene geometry, causing it to render incorrectly when the viewpoint moves. We present DyRAD, ...
| Authors | Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 September 2026 |
| Last updated at source | 1 October 2026 |
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- DyRAD/dyrad-syntheticDataset · cited by
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- DyRAD/dyrad-syntheticDataset
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