View-Independent Adjoint Light Tracing for Lighting Design Optimization

TL;DR

The paper tackles lighting-design optimization by removing camera dependence and introducing a view-independent differentiable rendering framework. It proposes an analytical adjoint light-tracing formulation built on a spatio-directional radiance data structure using hemi-spherical harmonics, enabling gradient-based optimization on luminaire parameters with GPU acceleration. The approach yields improved convergence and runtime over image-based methods, demonstrated across diverse scenes including offices, sculptures, baked-light refurbishments, and theatre stages, and it includes gradient visualization tools to analyze contributions. This work enables interactive, globally consistent lighting design directly in 3D, with practical impact for architectural visualization, stage design, and retrofitting baked lighting in real-world scenes.

Abstract

Differentiable rendering methods promise the ability to optimize various parameters of 3d scenes to achieve a desired result. However, lighting design has so far received little attention in this field. In this paper, 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 paper, we propose 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.

Figures (26)

• Figure 1: Comparing light tracing to path tracing on a large scene using our radiance data structure: While path tracing (a) would need more advanced sampling strategies to deal with a large non-uniform mesh, light tracing (b) naturally focuses samples on brighter areas, leading to a less noisy result and therefore improved optimization convergence (c) at equal runtime.
• Figure 2: Illustration of our adjoint light-tracing optimization. See also Fig. \ref{['fg:deriv-schematic']} for further details on the primal and adjoint data flow.
• Figure 3: Iterative refinement of the scene shown in Fig. \ref{['fg:large-office']}. First, desired changes are sketched on the 3d geometry (left), the optimization then adjusts the nearest light's position accordingly (right).
• Figure 4: Illustration of our primal and adjoint light tracing approach. In the primal pass, we trace radiant flux into the scene, while in the adjoint pass we collect derivatives of the objective function along the light path.
• Figure 5: Validation of our gradient formulation using finite difference approximations at various step sizes (for the light's 3d position in Fig. \ref{['fg:simpleOffice-opt-merge']}a). Our gradient closely matches FD approximations at adequate step sizes and we observe the expected trade-off between floating-point errors (too small FD steps) and approximation errors (too large FD steps). The naïve version, however, exhibits large systematic errors across a wide range of step sizes.