Adjoint nonlinear ray tracing

Adjoint nonlinear ray tracing
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伴随非线性光线追踪

DOI:
10.1145/3528223.3530077
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发表时间:
2022
影响因子:
6.2
通讯作者:
Gkioulekas, Ioannis
Gkioulekas, Ioannis
中科院分区:
计算机科学1区
文献类型:
--
作者:
Teh, Arjun;O'Toole, Matthew;Gkioulekas, Ioannis

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重建和设计具有连续变化的折射率场的介质仍然是计算机图形学中的一个具有挑战性的问题。试图解决这个逆问题的一个核心困难是光在这种介质内沿着曲线而不是直线传播。针对该问题的现有技术对介质内部光线的形状做出了强有力的假设,因此将其限制在光线偏转相对较小的介质上。最近,可微分渲染技术通过使可微分地模拟弯曲光路成为可能,放宽了这一限制。然而,这些技术背后的自动微分算法使用大量内存,将现有的可微分渲染技术限制在相对较小的介质和较低的空间分辨率上。我们提出了一种优化折射率场的方法,该方法既考虑了弯曲的光路,又具有小而恒定的内存占用。我们使用伴随状态方法导出一组方程,用于计算受非线性光线追踪约束的优化目标的折射率场的导数。我们还引入了离散化方案来对这些方程进行数值计算,而不需要在内存中存储非线性射线轨迹,从而显着降低了我们算法的内存需求。我们使用我们的技术来优化各种应用的高分辨率折射率场,包括创建不同类型的显示器(多视图、光场、焦散)、设计梯度折射率光学器件以及重建气流。
Reconstructing and designing media with continuously-varying refractive index fields remains a challenging problem in computer graphics. A core difficulty in trying to tackle this inverse problem is that light travels inside such media along curves, rather than straight lines. Existing techniques for this problem make strong assumptions on the shape of the ray inside the medium, and thus limit themselves to media where the ray deflection is relatively small. More recently, differentiable rendering techniques have relaxed this limitation, by making it possible to differentiably simulate curved light paths. However, the automatic differentiation algorithms underlying these techniques use large amounts of memory, restricting existing differentiable rendering techniques to relatively small media and low spatial resolutions.We present a method for optimizing refractive index fields that both accounts for curved light paths and has a small, constant memory footprint. We use the adjoint state method to derive a set of equations for computing derivatives with respect to the refractive index field of optimization objectives that are subject to nonlinear ray tracing constraints. We additionally introduce discretization schemes to numerically evaluate these equations, without the need to store nonlinear ray trajectories in memory, significantly reducing the memory requirements of our algorithm. We use our technique to optimize high-resolution refractive index fields for a variety of applications, including creating different types of displays (multiview, lightfield, caustic), designing gradient-index optics, and reconstructing gas flows.
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发表时间: 2020-07
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