Summary of Gs2pose: Two-stage 6d Object Pose Estimation Guided by Gaussian Splatting, By Jilan Mei et al.
GS2Pose: Two-stage 6D Object Pose Estimation Guided by Gaussian Splatting
by Jilan Mei, Junbo Li, Cai Meng
First submitted to arxiv on: 6 Nov 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 The proposed GS2Pose method enables accurate and robust 6D pose estimation of novel objects using only segmented RGBD images as input. This two-stage approach combines a lightweight U-Net network, Pose-Net, with a polarization attention mechanism for coarse estimation, followed by refined estimation using a pose regression algorithm, GS-Refiner. The latter leverages Lie algebra to extend 3D Gaussian splatting and selectively updates parameters in the model to adapt to environmental changes. Experimental results on the LineMod dataset demonstrate competitive performance compared to similar algorithms. The code will be released on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to recognize objects in 3D space using just images. It develops a new method called GS2Pose that can estimate the position and orientation of an object from pictures taken with a camera. This is helpful for tasks like robotic grasping or augmented reality. The method works by first estimating the object’s coarse pose, then refining it to get more accurate results. The approach is robust and can handle variations in lighting, occlusion, and other challenges. The researchers tested GS2Pose on a dataset of images and found that it performed well compared to similar methods. |
Keywords
» Artificial intelligence » Attention » Pose estimation » Regression
