Summary of Neural Implicit Representation For Building Digital Twins Of Unknown Articulated Objects, by Yijia Weng et al.
Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects
by Yijia Weng, Bowen Wen, Jonathan Tremblay, Valts Blukis, Dieter Fox, Leonidas Guibas, Stan Birchfield
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of creating digital twins of unknown articulated objects from two RGBD scans taken at different articulation states. The authors propose a two-stage approach, where the first stage reconstructs object-level shape at each state and the second stage recovers the underlying articulation model, including part segmentation and joint articulations that associate the two states. By incorporating point-level correspondences, image cues, 3D reconstructions, and kinematics, their method achieves more accurate and stable results compared to existing work. The approach can handle multiple movable parts and does not rely on prior knowledge of object shape or structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a digital twin of an unknown articulated object from two RGBD scans taken at different positions. It’s like taking a photo of something that can move, like a robot arm, from different angles. The authors use a special method to reconstruct the shape of the object and figure out how it moves between the two poses. They also make sure their method works well with objects that have multiple moving parts and doesn’t need any prior information about the object’s shape. |
