CVAug 7, 2024

PRTGS: Precomputed Radiance Transfer of Gaussian Splats for Real-Time High-Quality Relighting

arXiv:2408.03538v122 citationsh-index: 7
Originality Incremental advance
AI Analysis

This addresses the challenge of realistic real-time rendering for dynamic lighting in computer graphics, though it is incremental as it builds on existing 3D Gaussian splatting methods.

The paper tackled the problem of achieving real-time, high-quality relighting with soft shadows and interreflections for 3D Gaussian splats in low-frequency lighting environments, resulting in state-of-the-art visual quality at 30+ fps for 1080p resolution.

We proposed Precomputed RadianceTransfer of GaussianSplats (PRTGS), a real-time high-quality relighting method for Gaussian splats in low-frequency lighting environments that captures soft shadows and interreflections by precomputing 3D Gaussian splats' radiance transfer. Existing studies have demonstrated that 3D Gaussian splatting (3DGS) outperforms neural fields' efficiency for dynamic lighting scenarios. However, the current relighting method based on 3DGS still struggles to compute high-quality shadow and indirect illumination in real time for dynamic light, leading to unrealistic rendering results. We solve this problem by precomputing the expensive transport simulations required for complex transfer functions like shadowing, the resulting transfer functions are represented as dense sets of vectors or matrices for every Gaussian splat. We introduce distinct precomputing methods tailored for training and rendering stages, along with unique ray tracing and indirect lighting precomputation techniques for 3D Gaussian splats to accelerate training speed and compute accurate indirect lighting related to environment light. Experimental analyses demonstrate that our approach achieves state-of-the-art visual quality while maintaining competitive training times and allows high-quality real-time (30+ fps) relighting for dynamic light and relatively complex scenes at 1080p resolution.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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