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The aim is to improve the estimation of 6-DoF posture in automatic 3D part assembly.

HiFormer: Hierarchical Transformer with Box-packed Positional Encoding for 3D Part Assembly

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The aim is to improve the estimation of 6-DoF posture in automatic 3D part assembly.
Developed a multi-task 3D Swin Transformer with two-stage training for better feature extraction.
Created a hierarchical Transformer for capturing relationships between parts at various levels.
Introduced box-packed positional encoding that utilizes relative box positions for improved performance.
Achieved average improvements of 2.84% in Part Accuracy and 3.72% in Connection Accuracy under noisy conditions.
Obtained 3.55% in Part Accuracy and 3.21% in Connection Accuracy under deterministic conditions.

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Hierarchical Transformer improves 3D part assembly accuracy in robotics and computer vision, suggesting enhanced modeling methods.

Chen et al. (2026) studied this question.

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