GSCache: Real-Time Radiance Caching for Volume Path Tracing using 3D Gaussian Splatting

2025-07-25

AI Summary

Slow rendering speed and high noise levels plague volume path tracing in scientific visualization. To address these challenges, this paper proposes a real-time multi-level radiance caching method based on 3D Gaussian lattices. It introduces, for the first time, 3D Gaussian lattices as a path-space radiance cache structure, enabling online training and automatic adaptation to dynamic scene parameters. The method integrates volume path tracing with uniform sampling and next-event estimation, augmented by a lightweight online learning mechanism to accelerate cache updates. Experimental results demonstrate that the approach significantly reduces pixel variance while maintaining real-time computational overhead. It achieves superior rendering quality compared to conventional path tracing and state-of-the-art neural radiance caching methods. This work establishes a new paradigm for high-fidelity, low-latency scientific volume rendering.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Learning with Manifolds
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Real-time path tracing is rapidly becoming the standard for rendering in entertainment and professional applications. In scientific visualization, volume rendering plays a crucial role in helping researchers analyze and interpret complex 3D data. Recently, photorealistic rendering techniques have gained popularity in scientific visualization, yet they face significant challenges. One of the most prominent issues is slow rendering performance and high pixel variance caused by Monte Carlo integration. In this work, we introduce a novel radiance caching approach for path-traced volume rendering. Our method leverages advances in volumetric scene representation and adapts 3D Gaussian splatting to function as a multi-level, path-space radiance cache. This cache is designed to be trainable on the fly, dynamically adapting to changes in scene parameters such as lighting configurations and transfer functions. By incorporating our cache, we achieve less noisy, higher-quality images without increasing rendering costs. To evaluate our approach, we compare it against a baseline path tracer that supports uniform sampling and next-event estimation and the state-of-the-art for neural radiance caching. Through both quantitative and qualitative analyses, we demonstrate that our path-space radiance cache is a robust solution that is easy to integrate and significantly enhances the rendering quality of volumetric visualization applications while maintaining comparable computational efficiency.

Problem

• Slow rendering performance in volume path tracing
• High pixel variance from Monte Carlo integration
• Dynamic adaptation to scene parameter changes

Innovation

• 3D Gaussian splatting for radiance caching
• Trainable on-the-fly dynamic scene adaptation
• Noise reduction without increased rendering cost