View-Independent Adjoint Light Tracing for Lighting Design Optimization

Author:

Lipp Lukas1ORCID,Hahn David1ORCID,Ecormier-Nocca Pierre1ORCID,Rist Florian12ORCID,Wimmer Michael1ORCID

Affiliation:

1. Technische Universität Wien, Wien, Austria

2. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Abstract

Differentiable rendering methods promise the ability to optimize various parameters of three-dimensional (3D) scenes to achieve a desired result. However, lighting design has so far received little attention in this field. In this article, we introduce a method that enables continuous optimization of the arrangement of luminaires in a 3D scene via differentiable light tracing. Our experiments show two major issues when attempting to apply existing methods from differentiable path tracing to this problem: First, many rendering methods produce images, which restricts the ability of a designer to define lighting objectives to image space. Second, most previous methods are designed for scene geometry or material optimization and have not been extensively tested for the case of optimizing light sources. Currently available differentiable ray-tracing methods do not provide satisfactory performance, even on fairly basic test cases in our experience. In this article, we propose, to the best of our knowledge, a novel adjoint light tracing method that overcomes these challenges and enables gradient-based lighting design optimization in a view-independent (camera-free) way. Thus, we allow the user to paint illumination targets directly onto the 3D scene or use existing baked illumination data (e.g., light maps). Using modern ray-tracing hardware, we achieve interactive performance. We find light tracing advantageous over path tracing in this setting, as it naturally handles irregular geometry, resulting in less noise and improved optimization convergence. We compare our adjoint gradients to state-of-the-art image-based differentiable rendering methods. We also demonstrate that our gradient data works with various common optimization algorithms, providing good convergence behaviour. Qualitative comparisons with real-world scenes underline the practical applicability of our method.

Funder

Austrian Science Fund

Publisher

Association for Computing Machinery (ACM)

Reference55 articles.

1. ANSI/IES. 2020. IES Standard File Format for Photometric Data ANSI/IES LM-63-19. Retrieved from https://webstore.ansi.org/Standards/IESNA/ANSIIESLM6319

2. Sai Praveen Bangaru Tzu-Mao Li and Frédo Durand. 2020. Unbiased warped-area sampling for differentiable rendering. ACM Trans. Graph. 39 6 (2020) 1–18. DOI:10.1145/3414685.3417833

3. Andrew M. Bradley. 2019. PDE-constrained Optimization and the Adjoint Method. Retrieved from https://cs.stanford.edu/ambrad/adjoint_tutorial.pdf

4. DIAL GmbH. 2022. DIALux evo 10.1. Retrieved from https://www.dialux.com/en-GB/dialux

5. EasternGraphics GmbH. 2023. pCon.Planner. Retrieved from https://pcon-planner.com

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3