DPCS: Path Tracing-Based Differentiable Projector-Camera Systems

Jijiang Li;Qingyue Deng;Haibin Ling;Bingyao Huang
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Abstract

Projector-camera systems (ProCams) simulation aims to model the physical project-and-capture process and associated scene parameters of a ProCams, and is crucial for spatial augmented reality (SAR) applications such as ProCams relighting and projector compensation. Recent advances use an end-to-end neural network to learn the project-and-capture process. However, these neural network-based methods often implicitly encapsulate scene parameters, such as surface material, gamma, and white balance in the network parameters, and are less interpretable and hard for novel scene simulation. Moreover, neural networks usually learn the indirect illumination implicitly in an image-to-image translation way which leads to poor performance in simulating complex projection effects such as soft-shadow and interreflection. In this paper, we introduce a novel path tracing-based differentiable projector-camera systems (DPCS), offering a differentiable ProCams simulation method that explicitly integrates multi-bounce path tracing. Our DPCS models the physical project-and-capture process using differentiable physically-based rendering (PBR), enabling the scene parameters to be explicitly decoupled and learned using much fewer samples. Moreover, our physically-based method not only enables high-quality downstream ProCams tasks, such as ProCams relighting and projector compensation, but also allows novel scene simulation using the learned scene parameters. In experiments, DPCS demonstrates clear advantages over previous approaches in ProCams simulation, offering better interpretability, more efficient handling of complex interreflection and shadow, and requiring fewer training samples. The code and dataset are available on the project page: https://jijiangli.github.io/DPCS/.
基于路径跟踪的可微分投影-摄像机系统。
投影相机系统(ProCams)仿真旨在模拟物理项目和捕获过程以及ProCams的相关场景参数,这对于空间增强现实(SAR)应用(如ProCams重照明和投影仪补偿)至关重要。最近的进展是使用端到端神经网络来学习项目和捕获过程。然而,这些基于神经网络的方法通常隐式封装场景参数,如表面材料、伽马和白平衡在网络参数中,并且缺乏可解释性,难以用于新的场景模拟。此外,神经网络通常以图像到图像的转换方式隐式学习间接照明,这导致神经网络在模拟软阴影和互反射等复杂投影效果时性能不佳。本文介绍了一种新的基于路径跟踪的可微投影-摄像机系统(DPCS),提供了一种显式集成多反弹路径跟踪的可微ProCams仿真方法。我们的DPCS使用可微分的基于物理的渲染(PBR)对物理项目和捕获过程进行建模,使场景参数能够显式解耦并使用更少的样本进行学习。此外,我们基于物理的方法不仅可以实现高质量的下游ProCams任务,如ProCams重照明和投影仪补偿,还可以使用学习到的场景参数进行新颖的场景模拟。在实验中,DPCS在ProCams模拟中比以前的方法显示出明显的优势,提供更好的可解释性,更有效地处理复杂的互反射和阴影,并且需要更少的训练样本。代码和数据集可在项目页面上获得:https://jijiangli.github.io/DPCS/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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