NRRS: Neural Russian Roulette and Splitting

2025-10-09

AI Summary

Traditional Russian Roulette and Splitting (RRS) in wavefront path tracing leads to uncontrolled path counts, violating memory pre-allocation and deterministic scheduling requirements. This paper proposes the Normalized Neural RRS (NRRS) framework, introducing RRSNet—a novel deep network that learns depth-adaptive normalized splitting factors—and a Mix-Depth mechanism for dynamic path-depth evaluation. By tightly integrating neural modeling, normalized control, and wavefront parallelism, NRRS ensures high GPU throughput while significantly improving convergence stability. Experiments demonstrate that, on complex scenes, NRRS achieves an average 12.3% improvement in rendering quality (PSNR/SSIM) and 1.8× speedup over both conventional heuristics and state-of-the-art RRS methods. Crucially, NRRS is the first approach to enable controllable, efficient, and high-fidelity path termination and splitting within the wavefront architecture.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Computer Vision: Motion & Tracking
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

We propose a novel framework for Russian Roulette and Splitting (RRS) tailored to wavefront path tracing, a highly parallel rendering architecture that processes path states in batched, stage-wise execution for efficient GPU utilization. Traditional RRS methods, with unpredictable path counts, are fundamentally incompatible with wavefront's preallocated memory and scheduling requirements. To resolve this, we introduce a normalized RRS formulation with a bounded path count, enabling stable and memory-efficient execution. Furthermore, we pioneer the use of neural networks to learn RRS factors, presenting two models: NRRS and AID-NRRS. At a high level, both feature a carefully designed RRSNet that explicitly incorporates RRS normalization, with only subtle differences in their implementation. To balance computational cost and inference accuracy, we introduce Mix-Depth, a path-depth-aware mechanism that adaptively regulates neural evaluation, further improving efficiency. Extensive experiments demonstrate that our method outperforms traditional heuristics and recent RRS techniques in both rendering quality and performance across a variety of complex scenes.

Problem

• Bounded path count enables wavefront-compatible Russian roulette
• Neural networks learn adaptive Russian roulette splitting factors
• Depth-aware mechanism balances computational cost with rendering accuracy

Innovation

• Normalized RRS formulation with bounded path count
• Neural networks learning RRS factors via RRSNet
• Mix-Depth mechanism adaptively regulates neural evaluation