Deep Learning Adaptive Sampling for Faster Accurate Rendering
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Solution Overview
Problem
Existing rendering techniques face challenges in achieving high-quality animated content efficiently due to varying illumination and virtual content complexity across images, leading to uneven rendering and sub-optimal sampling strategies that do not account for denoising effects, resulting in prolonged computation times and resource inefficiencies.
Innovation Solution
Adaptive sampling using deep learning models to predict pixel values and generate sampling maps based on analytical distributions, incorporating denoising effects to optimize sample distribution across pixels, reducing the need for extensive training data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte-Carlo simulation techniques are used for light transport simulation, then rendering accuracy is improved, but rendering time increases significantly
Solution Approach 1:
The system performs preliminary analysis of the scene to identify regions requiring higher sampling density before executing the full rendering process. By pre-determining which areas need more samples based on scene complexity and illumination characteristics, the system avoids uniformly oversampling entire scenes, thus reducing total rendering time while maintaining accuracy in critical regions.
Solution Approach 2:
The patent implements adaptive sampling that assigns different sample densities to different regions of the image based on local characteristics. Complex regions with varying illumination or geometry receive higher sample counts for accuracy, while simple regions use fewer samples to save time. This local differentiation resolves the contradiction by optimizing the balance between accuracy and time on a per-region basis.
2Ease of operation
If uniform sampling is applied across all pixels, then simplicity of implementation is maintained, but rendering efficiency decreases due to over-sampling simple regions
Solution Approach 1:
The system transitions from static uniform sampling to dynamic adaptive sampling where sample distribution changes based on scene characteristics. The sampling strategy is adjusted dynamically during rendering based on detected features such as edges, illumination changes, and geometric complexity, allowing the system to automatically optimize efficiency without complex manual configuration.
Solution Approach 2:
The rendering system automatically analyzes its own scene characteristics and adjusts sampling distribution without external intervention. By self-evaluating scene complexity and autonomously determining optimal sample allocation, the system maintains ease of operation while dramatically improving rendering efficiency through intelligent resource allocation.
3Measurement precision
If higher sample counts are used per pixel, then color accuracy is improved, but computational cost increases
Solution Approach 1:
The system varies the sampling parameter (sample count per pixel) based on scene requirements rather than using a fixed value. By changing this parameter adaptively according to local scene characteristics such as illumination complexity, geometric detail, and material properties, the system achieves high color accuracy where needed while minimizing computational cost in simpler regions.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for adaptive sampling for rendering using deep learning. This includes receiving, at a sampler in a rendering pipeline, a plurality of rendered pixel data, wherein the sampler includes a first machine learning (ML) model. It further includes generating a sampling map for the rendering pipeline using the first ML model and the plurality of rendered pixel data, including predicting a plurality of pixel values in the sampling map based on a generated distribution of pixel values. It further includes rendering an image using the sampler, the sampling map, and a denoiser in the rendering pipeline.