Adjoint nonlinear ray tracing
Adjoint nonlinear ray tracing
复制标题
伴随非线性光线追踪
DOI:
10.1145/3528223.3530077
复制
发表时间:
2022
影响因子:
6.2
通讯作者:
Gkioulekas, Ioannis
中科院分区:
文献类型:
--
作者:
Teh, Arjun;O'Toole, Matthew;Gkioulekas, Ioannis
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.
登录
查看更多内容
DOI:
10.1109/ieeeconf35879.2020.9330342
发表时间:
2020-07
期刊:
2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting
影响因子:
--
作者:
M. Balasubramanian;S. Campbell;D. Werner
通讯作者:
M. Balasubramanian;S. Campbell;D. Werner
DOI:
--
发表时间:
2020
期刊:
arXiv.org
影响因子:
--
作者:
J. Stam
通讯作者:
J. Stam
DOI:
10.1145/2601097.2601200
发表时间:
2014-07
期刊:
ACM Transactions on Graphics (TOG)
影响因子:
--
作者:
Yuliy Schwartzburg;Romain Testuz;A. Tagliasacchi;M. Pauly
通讯作者:
Yuliy Schwartzburg;Romain Testuz;A. Tagliasacchi;M. Pauly
影响因子:
19.4
作者:
Scopelliti, Matteo Giuseppe;Chamanzar, Maysamreza
通讯作者:
Chamanzar, Maysamreza
DOI:
--
发表时间:
--
期刊:
影响因子:
--
作者:
N. Heybeli;F. Oktar;S. Ozyazgan;G. Akkan;S. Ozsoy
通讯作者:
S. Ozsoy