Summary of 3d Geometric Shape Assembly Via Efficient Point Cloud Matching, by Nahyuk Lee et al.
3D Geometric Shape Assembly via Efficient Point Cloud Matching
by Nahyuk Lee, Juhong Min, Junha Lee, Seungwook Kim, Kanghee Lee, Jaesik Park, Minsu Cho
First submitted to arxiv on: 15 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 This paper tackles the problem of assembling geometric shapes into larger target structures, a crucial task in various practical applications. The authors introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and computation. Building upon PMT, the authors propose Proxy Match Transformer (PMTR) for the geometric assembly task. They evaluate PMTR on a large-scale 3D geometric shape assembly benchmark dataset, demonstrating its superior performance and efficiency compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about building things from smaller parts into bigger structures. It’s an important problem that has many practical uses. The authors created a new way to match the shapes of these parts together called Proxy Match Transform (PMT). They used this method to create a new framework for building things, called PMTR. This new approach was tested on a big dataset and showed it can do its job better than other methods. |
Keywords
» Artificial intelligence » Transformer
