SE(3)-bi-equivariant Transformers for Point Cloud Assembly
Paper in proceeding, 2024

Given a pair of point clouds, the goal of assembly is to recover a rigid transformation that aligns one point cloud to the other. This task is challenging because the point clouds may be non-overlapped, and they may have arbitrary initial positions. To address these difficulties, we propose a method, called SE(3)-bi-equivariant transformer (BITR), based on the SE(3)-bi-equivariance prior of the task: it guarantees that when the inputs are rigidly perturbed, the output will transform accordingly. Due to its equivariance property, BITR can not only handle non-overlapped PCs, but also guarantee robustness against initial positions. Specifically, BITR first extracts features of the inputs using a novel SE(3)×SE(3)-transformer, and then projects the learned feature to group SE(3) as the output. Moreover, we theoretically show that swap and scale equivariances can be incorporated into BITR, thus it further guarantees stable performance under scaling and swapping the inputs. We experimentally show the effectiveness of BITR in practical tasks.

Author

Ziming Wang

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Rebecka Jörnsten

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Advances in Neural Information Processing Systems

10495258 (ISSN)

Vol. 37

38th Conference on Neural Information Processing Systems, NeurIPS 2024
Vancouver, Canada,

Subject Categories (SSIF 2025)

Computer graphics and computer vision

Computer Sciences

Signal Processing

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Latest update

4/3/2025 1