Summary of Dart: Implicit Doppler Tomography For Radar Novel View Synthesis, by Tianshu Huang et al.
DART: Implicit Doppler Tomography for Radar Novel View Synthesis
by Tianshu Huang, John Miller, Akarsh Prabhakara, Tao Jin, Tarana Laroia, Zico Kolter, Anthony Rowe
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A Neural Radiance Field-inspired method for simulating realistic radar scans is proposed in this paper. The method, called Doppler Aided Radar Tomography (DART), uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. DART is evaluated by collecting a novel radar dataset with accurate position and instantaneous velocity measurements from lidar-based localization. Compared to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets and can be used to generate high-quality tomographic images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new method called Doppler Aided Radar Tomography (DART) that helps designers create realistic simulations of radar scans. The goal is to make it easier to test different algorithms for tasks like imaging, detecting targets, classifying objects, and tracking movements. To do this, DART uses special physics about how radar works to create a way to render images from range-Doppler data. The method is tested using a new dataset of real-world radar data that also includes accurate information about the position and movement of objects. |
Keywords
* Artificial intelligence * Tracking
