端到端全投影仪补偿
End-to-end Full Projector Compensation
摘要：
全投影仪补偿旨在修改投影仪输入图像以补偿投影表面的几何和光度干扰。传统的方法通常分别解决这两个部分，并可能遭受次优的解决方案。在本文中，我们提出了第一个端到端的可微解决方案，名为CompensNeSt ++，共同解决这两个问题。首先，我们提出了一种新的几何校正子网，命名为WarpingNet，它被设计为级联的粗到细结构，直接从采样图像中学习采样网格。其次，我们提出了一种新的光度补偿子网，名为CompensNeSt，它是设计与连体架构，以捕捉投影表面和投影图像之间的光度相互作用，并使用这些信息来补偿几何校正的图像。通过将WarpingNet与CompensNeSt相结合，CompensNeSt ++实现了完整的投影仪补偿，并且是端到端可训练的。第三，为了提高实用性，我们提出了一种新的基于合成数据的预训练策略，以显着减少训练图像的数量和训练时间。此外，我们构建了第一个独立于设置的完全补偿基准，以方便未来的研究。在深入的实验中，我们的方法显示出明显的优势，与现有技术的有前途的补偿质量，同时是实用方便。
英文摘要：
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface. Traditional methods usually solve the two parts separately and may suffer from suboptimal solutions. In this paper, we propose the first end-to-end differentiable solution, named CompenNeSt++, to solve the two problems jointly. First, we propose a novel geometric correction subnet, named WarpingNet, which is designed with a cascaded coarse-to-fine structure to learn the sampling grid directly from sampling images. Second, we propose a novel photometric compensation subnet, named CompenNeSt, which is designed with a siamese architecture to capture the photometric interactions between the projection surface and the projected images, and to use such information to compensate the geometrically corrected images. By concatenating WarpingNet with CompenNeSt, CompenNeSt++ accomplishes full projector compensation and is end-to-end trainable. Third, to improve practicability, we propose a novel synthetic data-based pre-training strategy to significantly reduce the number of training images and training time. Moreover, we construct the first setup-independent full compensation benchmark to facilitate future studies. In thorough experiments, our method shows clear advantages over prior art with promising compensation quality and meanwhile being practically convenient.
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