Summary of Domain-transferred Synthetic Data Generation For Improving Monocular Depth Estimation, by Seungyeop Lee et al.
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation
by Seungyeop Lee, Knut Peterson, Solmaz Arezoomandan, Bill Cai, Peihan Li, Lifeng Zhou, David Han
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 The proposed method uses synthetic 3D environments and CycleGAN domain transfer to generate high-quality depth data for monocular depth estimation. This addresses the difficulty of collecting accurate depth data that corresponds to RGB images, which is time-consuming and costly. The method is compared to the popular NYUDepth V2 dataset by training a depth estimation model based on the DenseDepth structure using different training sets of real and simulated data. The performance of the models is evaluated on newly collected images and LiDAR depth data from a Husky robot, showing that GAN-transformed data can be an effective alternative to real-world data for depth estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to help machines understand what’s in front of them without needing special equipment. Right now, it takes a lot of time and money to get good pictures of how far away things are from the machine. This new method uses computer-made worlds and special tricks to create fake data that can be used instead. This fake data is tested against real data taken by a robot to make sure it works well. The results show that this new way can be just as good as using real data, which could make things easier and cheaper for machines. |
Keywords
» Artificial intelligence » Depth estimation » Gan
