Summary of Augmented Reality Based Simulated Data (arsim) with Multi-view Consistency For Av Perception Networks, by Aqeel Anwar et al.
Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks
by Aqeel Anwar, Tae Eun Choe, Zian Wang, Sanja Fidler, Minwoo Park
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 Detecting diverse objects under various driving scenarios is crucial for effective autonomous vehicles. However, real-world data often lacks diversity, resulting in a long-tail distribution. To overcome this challenge, researchers propose ARSim, a fully automated framework that enhances real multi-view image data with 3D synthetic objects of interest. The method integrates domain adaptation and randomization strategies to address the covariate shift between real and simulated data. It constructs a virtual scene using real data and strategically places synthetic assets within it. Illumination is achieved by estimating light distribution from multiple images capturing the surroundings of the vehicle. Camera parameters from real data are employed to render synthetic assets in each frame. The resulting augmented dataset is used to train a multi-camera perception network for autonomous vehicles. Experimental results on various AV perception tasks demonstrate the superior performance of networks trained on the augmented dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous cars need to recognize many different things while driving, like other cars, pedestrians, and road signs. But the data we collect from real-world driving often lacks variety, making it hard for computers to learn. To fix this problem, researchers created a new tool called ARSim that adds fake objects to real images and videos. This helps train computer models to recognize things better. The tool uses real data to make virtual scenes and places fake objects in them. It also makes the lighting look like what the car would see on the road. Then, it uses these fake images to train a special computer model that can help drive cars safely. The results show that this new approach helps computer models perform better than before. |
Keywords
* Artificial intelligence * Domain adaptation
