Summary of Close the Sim2real Gap Via Physically-based Structured Light Synthetic Data Simulation, by Kaixin Bai et al.
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation
by Kaixin Bai, Lei Zhang, Zhaopeng Chen, Fang Wan, Jianwei Zhang
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel approach to deep learning in industrial robotics is proposed, addressing the limitations of current sim2real methods. The authors introduce a physically-based structured light simulation system that generates realistic RGB and depth images, surpassing existing dataset generation tools. This system enables the creation of an RGBD dataset tailored for robotic grasping scenarios, which is evaluated across various tasks including object detection, instance segmentation, and embedding visual perception in industrial robotic grasping. The proposed approach reduces the sim2real gap and enhances deep learning training, facilitating the application of deep learning models in industrial settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are working to make robots better at doing jobs on assembly lines and other industrial sites. They’re using a special kind of computer vision called deep learning, but it’s hard to get it to work well with real-world data. To solve this problem, they created a new way to generate fake data that looks like the real thing. This allows them to train their computers to recognize objects and perform tasks more accurately. The goal is to make robots smarter and more efficient at doing industrial jobs. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Instance segmentation » Object detection
