CSPR-Net：用于非平面投影几何失真校正的自监督曲面投影校正网络
CSPR-Net: Self-supervised Curved Surface Projection Rectification Network for Geometric Distortion Correction in Non-planar Projections
AI总结：
CSPR-Net通过自监督深度学习框架实现非平面投影中的几何失真自动校正，提升投影图像的保真度和精度。
摘要：
将图像投影到非平面表面不可避免地引入几何失真，这会降低视觉质量。传统校正方法通常需要繁琐的手动校准或结构化光序列来建立像素级对应关系。在本文中，我们开发了曲面投影校正网络（CSPR-Net），这是一种自监督深度学习框架，用于自动化失真校正。我们的方法采用双坐标基于神经网络，学习投影仪和摄像机空间之间的双向映射。通过施加强大的循环一致性约束，CSPR-Net可以自主解决复杂的几何变换，而无需地面真实变形场。此外，引入了基于梯度的损失函数以减轻复杂环境光干扰，并准确捕捉高频几何变化。在物理实验场景中的定量评估表明，CSPR-Net在端到端保真度（SSIM）上实现了20.7%的提升，并在正向和反向映射方面分别比多项式基线高出3.8%和5.4%，有效地生成高精度预扭曲图像以实现无缝投影。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
