BOE-ViT: Boosting Orientation Estimation with Equivariance in Self-Supervised 3D Subtomogram Alignment
Jiang, Runmin Daggett, Jackson Pingulkar, Shriya Zhao, Yizhou Dhingra, Priyanshu Brown, Daniel Wu, Qifeng Zeng, Xiangrui Li, Xingjian Xu, Min
Jiang, Runmin
Daggett, Jackson
Pingulkar, Shriya
Zhao, Yizhou
Dhingra, Priyanshu
Brown, Daniel
Wu, Qifeng
Zeng, Xiangrui
Li, Xingjian
Xu, Min
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Abstract
Subtomogram alignment is a critical task in cryo-electron tomography (cryo-ET) analysis, essential for achieving high-resolution reconstructions of macromolecular complexes. However, learning effective positional representations remains challenging due to limited labels and high noise levels inherent in cryo-ET data. In this work, we address this challenge by proposing a self-supervised learning approach that leverages intrinsic geometric transformations as implicit supervisory signals, enabling robust representation learning despite data scarcity. We introduce BOE-ViT, the first Vision Transformer (ViT) framework for 3D subtomogram alignment. Recognizing that traditional ViTs lack equivariance and are therefore suboptimal for orientation estimation, we enhance the model with two innovative modules that introduce equivariance include 1) the Polyshift module for improved shift estimation and 2) Multi-Axis Rotation Encoding (MARE) for enhanced rotation estimation. Experimental results demonstrate that BOE-ViT significantly outperforms state-of-the-art methods. Notably, at SNR 0.01 dataset, our approach achieves a 77.3% reduction in rotation estimation error and a 62.5% reduction in translation estimation error, effectively overcoming the challenges in cryo-ET subtomogram alignment.
Citation
R. Jiang et al., “BOE-ViT: Boosting Orientation Estimation with Equivariance in Self-Supervised 3D Subtomogram Alignment,” 2025
Source
Proceedings of the Computer Vision and Pattern Recognition Conference
Conference
Computer Vision and Pattern Recognition Conference (CVPR), 2025
Keywords
Subjects
Source
Computer Vision and Pattern Recognition Conference (CVPR), 2025
Publisher
Computer Vision Foundation