Summary of Rdpn6d: Residual-based Dense Point-wise Network For 6dof Object Pose Estimation Based on Rgb-d Images, by Zong-wei Hong et al.
RDPN6D: Residual-based Dense Point-wise Network for 6Dof Object Pose Estimation Based on RGB-D Images
by Zong-Wei Hong, Yen-Yang Hung, Chu-Song Chen
First submitted to arxiv on: 14 May 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 calculating the 6DoF pose of an object using a single RGB-D image is introduced. The method regresses the object coordinates for each visible pixel, leveraging existing object detection methods and incorporating a re-projection mechanism to adjust the camera’s intrinsic matrix. A transformation to residual representation reduces the output space, yielding superior performance. Extensive experiments demonstrate the efficacy of this approach, outperforming most previous methods, especially in occlusion scenarios. The code is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to find an object’s position and orientation using just one image from a camera that can see both color and depth. Unlike other methods that try to predict the object’s pose directly or use sparse points for recovery, this method uses dense correspondence to calculate the object coordinates for each visible pixel. It also includes a mechanism to adjust the camera’s settings based on cropping in the RGB-D images. The results show that this approach works well and outperforms other methods, especially when parts of the object are hidden. |
Keywords
» Artificial intelligence » Object detection
