Summary of Se(3)-bi-equivariant Transformers For Point Cloud Assembly, by Ziming Wang et al.
SE(3)-bi-equivariant Transformers for Point Cloud Assembly
by Ziming Wang, Rebecka Jörnsten
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel method called SE(3)-bi-equivariant transformer (BITR) to solve the challenging task of assembly, which involves recovering a rigid transformation that aligns one point cloud to another. The proposed method is based on the SE(3)-bi-equivariance prior of the task, ensuring that when the inputs are rigidly perturbed, the output will transform accordingly. BITR can handle non-overlapped point clouds and guarantee robustness against initial positions by first extracting features using a novel SE(3) SE(3)-transformer and then projecting the learned feature to group SE(3) as the output. Furthermore, the paper theoretically shows that swap and scale equivariances can be incorporated into BITR, ensuring stable performance under scaling and swapping the inputs. The effectiveness of BITR is experimentally demonstrated in practical tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a tricky problem called assembly, where you try to match two sets of 3D points together. This is hard because the points might not overlap, and they could be in different positions. To make it easier, the authors created a new way to do this called BITR (SE(3)-bi-equivariant transformer). BITR works by taking features from the input point clouds and using them to figure out how to match the points together. This method is special because it can handle cases where the points don’t overlap or are in different positions. The authors also show that their method is good at dealing with changes like scaling or swapping the inputs. |
Keywords
* Artificial intelligence * Transformer
