Filter-based monte carlo gradient path tracing rendering image reconstruction method and device

By obtaining the initial image and auxiliary information map of the Monte Carlo gradient path tracing rendering, and using the gradient and filtering optimization objective function to generate multi-resolution filtering results, the problems of poor image quality and insufficient consistency of the Monte Carlo gradient path rendering method at low sampling numbers are solved, and efficient and stable image reconstruction effects are achieved.

CN119579420BActive Publication Date: 2025-10-10TSINGHUA UNIVERSITY
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Patent Information

Application Number
CN202411503682.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-25
Publication Date
2025-10-10
Estimated Expiration
2044-10-25

AI Technical Summary

Technical Problem

Existing Monte Carlo gradient path rendering methods perform poorly at low sampling numbers and cannot guarantee the consistency of calculation results in different scenarios. In particular, the image quality generated by the Poisson equation-based method is unstable at low sampling numbers, and the generalization ability of the neural network method is unstable.

Method used

By obtaining the initial rendered image and auxiliary information map obtained by Monte Carlo gradient path tracing rendering, the optimized gradient is solved using the gradient optimization objective function, and multi-resolution levels are generated. Combined with the filtering optimization objective function, filtering calculations are performed layer by layer to generate high-quality and consistent denoised images.

Benefits of technology

It generates high-quality and consistent denoised images at a lower sampling rate, reduces the impact of noise in gradient estimation, has good generalization performance, and meets the needs of high-efficiency and high-quality image generation.

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Abstract

The application provides a filter-based Monte Carlo gradient path tracking rendering image reconstruction method and device, wherein the method comprises the following steps: obtaining an initial rendering image obtained by Monte Carlo gradient path tracking rendering, and auxiliary information map in the rendering process; solving an optimized gradient by a gradient optimization objective function according to the initial rendering image and the auxiliary information map, and generating a multi-resolution level; performing filter calculation on the initial rendering image, the optimized gradient and the auxiliary information map according to the multi-resolution level from the lowest resolution level to the highest resolution level to obtain a target filter result; wherein the target filter result is a filter result output by the highest resolution level, and the filter result output by the highest resolution level is a reconstruction result of the initial rendering image. The method fully utilizes the gradient domain information and the auxiliary information map in the rendering process, solves the filter kernel coefficient at a resolution level, generates a high-quality and consistent denoising image (reconstruction result) at a low sampling rate, reduces the influence of noise in the gradient estimation on the reconstruction result, has a series of good characteristics such as consistency (gradual convergence to a reference image), and meets the efficient and high-quality rendering image reconstruction requirements.
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