WishGI: Lightweight Static Global Illumination Baking via Spherical Harmonics Fitting

TL;DR

WishGI introduces a lightweight static global illumination baking method that uses spherical harmonics fitting and inverse probe distribution to achieve high-quality lighting with minimal memory and computational overhead, suitable for low-end devices.

Contribution

The paper presents a novel spherical harmonics fitting approach combined with inverse probe distribution for efficient, low-memory static global illumination baking.

Findings

Uses only ~5% of memory compared to industry standards.

Reduces sampling in fragment shader without extra render passes.

Maintains consistent lighting quality across mesh instances.

Abstract

Global illumination combines direct and indirect lighting to create realistic lighting effects, bringing virtual scenes closer to reality. Static global illumination is a crucial component of virtual scene rendering, leveraging precomputation and baking techniques to significantly reduce runtime computational costs. Unfortunately, many existing works prioritize visual quality by relying on extensive texture storage and massive pixel-level texture sampling, leading to large performance overhead. In this paper, we introduce an illumination reconstruction method that effectively reduces sampling in fragment shader and avoids additional render passes, making it well-suited for low-end platforms. To achieve high-quality global illumination with reduced memory usage, we adopt a spherical harmonics fitting approach for baking effective illumination information and propose an inverse probe distribution method that generates unique probe associations for each mesh. This association, which can be generated offline in the local space, ensures consistent lighting quality across all instances of the same mesh. As a consequence, our method delivers highly competitive lighting effects while using only approximately 5% of the memory required by mainstream industry techniques.