Add to ORKGAbstract: Monte-Carlo methods of ray tracing allow generating physically correct images of virtual scenes, reproducing a large number of optical effects. However, the resulting image contains noise inherent in stochastic algorithms. An original method of denoising in these images is proposed. Small-scale details of the image are not distorted because they are taken from an auxiliary, reference image constructed by a deterministic ray tracing. The reference image allows you to extract the secondary lighting component which is usually a smooth function and allows for efficient filtering. After multiplying its result by an auxiliary deterministic image, a sharp picture is obtained with preservation of small details and with greatly r...
Stochastic ray tracing methods have become the industry's standard for today's realistic image synth...
Progressive stochastic ray tracing algorithms are increasingly used in in-teractive applications suc...
The problem of accurately simulating light transport using Monte Carlo integration can be very diffi...
Abstract: Bidirectional Monte-Carlo methods of ray tracing allow you to effectively genera...
Abstract: This paper presents a method of filtering using several input images obtained by...
Monte Carlo rendering systems can produce important visual effects such as depth of field, motion bl...
This paper introduces a novel stochastic approach to image denoising using an adaptive Monte Carlo s...
Producing photorealistic images from a scene model requires computing a complex multidimensional int...
International audienceMonte Carlo based methods such as path tracing are widely used in movie produc...
With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a ne...
A proposed method reduces noise in stochastic ray tracing for interactive progressive rendering. The...
We present an improved version of a state-of-the-art noise reduction technique for progressive stoch...
We present an improved version of a state-of-the-art noise reduction technique for progressive stoch...
Image filtering is often applied as a post-process to Monte Carlo generated pictures, in order to re...
Image filtering is often applied as a post-process to Monte Carlo generated pictures, in order to re...
Stochastic ray tracing methods have become the industry's standard for today's realistic image synth...
Progressive stochastic ray tracing algorithms are increasingly used in in-teractive applications suc...
The problem of accurately simulating light transport using Monte Carlo integration can be very diffi...
Abstract: Bidirectional Monte-Carlo methods of ray tracing allow you to effectively genera...
Abstract: This paper presents a method of filtering using several input images obtained by...
Monte Carlo rendering systems can produce important visual effects such as depth of field, motion bl...
This paper introduces a novel stochastic approach to image denoising using an adaptive Monte Carlo s...
Producing photorealistic images from a scene model requires computing a complex multidimensional int...
International audienceMonte Carlo based methods such as path tracing are widely used in movie produc...
With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a ne...
A proposed method reduces noise in stochastic ray tracing for interactive progressive rendering. The...
We present an improved version of a state-of-the-art noise reduction technique for progressive stoch...
We present an improved version of a state-of-the-art noise reduction technique for progressive stoch...
Image filtering is often applied as a post-process to Monte Carlo generated pictures, in order to re...
Image filtering is often applied as a post-process to Monte Carlo generated pictures, in order to re...
Stochastic ray tracing methods have become the industry's standard for today's realistic image synth...
Progressive stochastic ray tracing algorithms are increasingly used in in-teractive applications suc...
The problem of accurately simulating light transport using Monte Carlo integration can be very diffi...