Filter-adapted spatiotemporal sampling for real-time rendering (2310.15364v1)

Abstract: Stochastic sampling techniques are ubiquitous in real-time rendering, where performance constraints force the use of low sample counts, leading to noisy intermediate results. To remove this noise, the post-processing step of temporal and spatial denoising is an integral part of the real-time graphics pipeline. The main insight presented in this paper is that we can optimize the samples used in stochastic sampling such that the post-processing error is minimized. The core of our method is an analytical loss function which measures post-filtering error for a class of integrands - multidimensional Heaviside functions. These integrands are an approximation of the discontinuous functions commonly found in rendering. Our analysis applies to arbitrary spatial and spatiotemporal filters, scalar and vector sample values, and uniform and non-uniform probability distributions. We show that the spectrum of Monte Carlo noise resulting from our sampling method is adapted to the shape of the filter, resulting in less noisy final images. We demonstrate improvements over state-of-the-art sampling methods in three representative rendering tasks: ambient occlusion, volumetric ray-marching, and color image dithering. Common use noise textures, and noise generation code is available at https://github.com/electronicarts/fastnoise.

• The paper’s main contribution is the formulation of an analytical loss function that minimizes post-filtering error in Monte Carlo sampling.
• It tailors sample distributions to match specific spatial and temporal filters, effectively reducing noise artifacts in ambient occlusion and volumetric ray-marching.
• Experimental results show lower RMSE and enhanced image quality, demonstrating practical improvements for real-time graphics applications.

An Analysis of Filter-Adapted Spatiotemporal Sampling for Real-Time Rendering

This paper introduces a compelling approach to optimizing stochastic sampling for real-time rendering applications, wherein the inherent noise from low sample counts necessitates effective denoising strategies. The paper posits that the selection and tailoring of sample distributions based on the specific filtering techniques employed can significantly reduce post-processing errors. This paper is anchored in the context of improving methods like Monte Carlo (MC) sampling, which are prevalent in rendering high-dimensional integrands but limited by performance constraints in real-time settings.

Key Methodological Insights

The core methodological advancement of this paper lies in the formulation of an analytical loss function designed to minimize post-filtering error across various integrands, modeled here as multidimensional Heaviside functions. These functions serve as a proxy for the discontinuous characteristics typical in rendering. The loss function encompasses:

• Consideration of arbitrary spatial and temporal filters.
• Adaptation to both scalar and vector sample spaces.
• Flexibility between uniform and non-uniform probability distributions.

The result is an optimized sampling method that aligns the noise spectrum of MC noise with the filter's shape, leading to improved image quality after denoising.

Experimental Evaluation

The authors validate their method against many state-of-the-art sampling techniques across different rendering tasks:

1. Ambient Occlusion: Utilizing cosine-weighted hemispherical samples which are further optimized over space and time using exponential moving averaging (EMA) and spatial box blur. The method shows reduced visual artifacts and lower root mean square error (RMSE) compared to existing spatiotemporal blue noise (STBN) methods.
2. Volumetric Ray-Marching: The FAST noise family developed in this research is compared against STBN, with evaluation metrics indicating a more favorable noise distribution that efficiently minimizes post-processing errors.
3. Color Image Dithering: Demonstrates the adaptability of the noise textures generated using the proposed method to provide high visual fidelity and reduced noise perception.

Implications and Future Directions

The implications of this work are twofold. Practically, rendering engines can benefit from more efficient sampling, resulting in fewer artifacts and improved visual quality, which is crucial for real-time applications such as gaming. Theoretically, this research enriches the understanding of noise optimization in rendering, encouraging further exploration into sampling techniques that adapt intimately with the kernel of the desired filter.

Looking forward, speculative extensions of this work could involve:

• Investigation of the optimal properties of noise for discontinuous mappings, where sample-space transformations degrade typical blue noise properties.
• Exploration of neural network-based denoising pipelines jointly optimized with this sampling approach.
• Evaluating the perceptual impacts of different noise spectra, especially in time-critical rendering scenarios.

This paper significantly progresses the domain of optimized spatiotemporal sampling, providing a rigorous foundation for subsequent explorations and practical applications in the field of real-time computer graphics rendering.