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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.08433 |
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| _version_ | 1866915785955344384 |
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| author | Peng, Kejin Wei, Jia Hao, Xiang |
| author_facet | Peng, Kejin Wei, Jia Hao, Xiang |
| contents | Projecting images onto non-planar surfaces inevitably introduces geometric distortions that degrade visual quality. Traditional correction methods often require tedious manual calibration or structured light sequences to establish pixel-wise correspondences. In this paper, we develop the Curved Surface Projection Rectification Network (CSPR-Net), a self-supervised deep learning framework for automated distortion correction. Our approach employs dual coordinate-based neural networks to learn the bi-directional mapping between the projector and camera spaces. By enforcing a robust cycle-consistency constraint, CSPR-Net autonomously resolves complex geometric transformations without requiring ground-truth deformation fields. Furthermore, a gradient-based loss function is introduced to mitigate the impact of complex ambient light interference and accurately capture high-frequency geometric variations. Quantitative evaluations in physical experimental scenarios demonstrate that CSPR-Net achieves a 20.7% improvement in end-to-end fidelity (SSIM) and outperforms the polynomial baseline by 3.8% and 5.4% in forward and inverse mapping in terms of SSIM respectively, effectively generating high-precision pre-warped images for seamless projection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08433 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CSPR-Net: Self-supervised Curved Surface Projection Rectification Network for Geometric Distortion Correction in Non-planar Projections Peng, Kejin Wei, Jia Hao, Xiang Optics Projecting images onto non-planar surfaces inevitably introduces geometric distortions that degrade visual quality. Traditional correction methods often require tedious manual calibration or structured light sequences to establish pixel-wise correspondences. In this paper, we develop the Curved Surface Projection Rectification Network (CSPR-Net), a self-supervised deep learning framework for automated distortion correction. Our approach employs dual coordinate-based neural networks to learn the bi-directional mapping between the projector and camera spaces. By enforcing a robust cycle-consistency constraint, CSPR-Net autonomously resolves complex geometric transformations without requiring ground-truth deformation fields. Furthermore, a gradient-based loss function is introduced to mitigate the impact of complex ambient light interference and accurately capture high-frequency geometric variations. Quantitative evaluations in physical experimental scenarios demonstrate that CSPR-Net achieves a 20.7% improvement in end-to-end fidelity (SSIM) and outperforms the polynomial baseline by 3.8% and 5.4% in forward and inverse mapping in terms of SSIM respectively, effectively generating high-precision pre-warped images for seamless projection. |
| title | CSPR-Net: Self-supervised Curved Surface Projection Rectification Network for Geometric Distortion Correction in Non-planar Projections |
| topic | Optics |
| url | https://arxiv.org/abs/2602.08433 |