Forget Superresolution, Sample Adaptively (when Path Tracing)

2026-02-09

AI Summary

This work proposes an end-to-end adaptive sampling and denoising framework tailored for real-time path tracing under extremely low sampling budgets—typically below one sample per pixel. Conventional super-resolution methods struggle with spatially varying noise, reconstruction difficulty, and perceptual importance, often leading to detail loss. To address this, the framework employs a differentiable stochastic sampling strategy that enables gradient estimation through discrete sampling decisions, coupled with perceptually driven tone-mapped training to avoid oversampling in visually insensitive regions. It further integrates a pyramid-aggregation denoising filter and a learnable albedo demodulation module. Experiments demonstrate that the method significantly outperforms uniform sampling, with particularly notable improvements in perceptually critical areas such as specular highlights and shadow boundaries.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Real-time path tracing increasingly operates under extremely low sampling budgets, often below one sample per pixel, as rendering complexity, resolution, and frame-rate requirements continue to rise. While super-resolution is widely used in production, it uniformly sacrifices spatial detail and cannot exploit variations in noise, reconstruction difficulty, and perceptual importance across the image. Adaptive sampling offers a compelling alternative, but existing end-to-end approaches rely on approximations that break down in sparse regimes. We introduce an end-to-end adaptive sampling and denoising pipeline explicitly designed for the sub-1-spp regime. Our method uses a stochastic formulation of sample placement that enables gradient estimation despite discrete sampling decisions, allowing stable training of a neural sampler at low sampling budgets. To better align optimization with human perception, we propose a tonemapping-aware training pipeline that integrates differentiable filmic operators and a state-of-the-art perceptual loss, preventing oversampling of regions with low visual impact. In addition, we introduce a gather-based pyramidal denoising filter and a learnable generalization of albedo demodulation tailored to sparse sampling. Our results show consistent improvements over uniform sparse sampling, with notably better reconstruction of perceptually critical details such as specular highlights and shadow boundaries, and demonstrate that adaptive sampling remains effective even at minimal budgets.

Problem

• adaptive sampling
• path tracing
• low sample budget
• perceptual importance
• sparse sampling

Innovation

• adaptive sampling
• path tracing
• perceptual loss
• differentiable rendering
• sparse denoising