Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection

• URL: http://arxiv.org/abs/2503.20235v2
• Date: Thu, 27 Mar 2025 02:40:25 GMT
• Title: Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection
• Authors: Ahyun Seo, Minsu Cho,
• Abstract summary: This paper focuses on rotation symmetry, where objects remain unchanged when rotated around a central axis.<n>Traditional methods relied on hand-crafted feature matching, while recent segmentation models based on convolutional neural networks detect rotation centers but struggle with 3D geometric consistency.<n>We propose a model that directly predicts rotation centers and vertices in 3D space and projects the results back to 2D while preserving structural integrity.
• Score: 48.11373832295736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symmetry plays a vital role in understanding structural patterns, aiding object recognition and scene interpretation. This paper focuses on rotation symmetry, where objects remain unchanged when rotated around a central axis, requiring detection of rotation centers and supporting vertices. Traditional methods relied on hand-crafted feature matching, while recent segmentation models based on convolutional neural networks detect rotation centers but struggle with 3D geometric consistency due to viewpoint distortions. To overcome this, we propose a model that directly predicts rotation centers and vertices in 3D space and projects the results back to 2D while preserving structural integrity. By incorporating a vertex reconstruction stage enforcing 3D geometric priors -- such as equal side lengths and interior angles -- our model enhances robustness and accuracy. Experiments on the DENDI dataset show superior performance in rotation axis detection and validate the impact of 3D priors through ablation studies.

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