# TU Wien Rendering #36 - Vertex Connection and Merging, Path Space Regularization ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=Hc9zu5-O7Eo - **Дата:** 05.06.2015 - **Длительность:** 5:05 - **Просмотры:** 4,358 - **Источник:** https://ekstraktznaniy.ru/video/14976 ## Описание The two main branches of global illumination algorithms were biased and unbiased techniques. Iliyan Georgiev came up with a method that finally, after so long, unifies these two worlds. The algorithm starts out by cutting some corners, while progressively decreasing the bias as time goes by. About the course: This course aims to give an overview of basic and state-of-the-art methods of rendering. Offline methods such as ray and path tracing, photon mapping and many other algorithms are introduced and various refinement are explained. The basics of the involved physics, such as geometric optics, surface and media interaction with light and camera models are outlined. The apparatus of Monte Carlo methods is introduced which is heavily used in several algorithms and its refinement in the form of stratified sampling and the Metropolis-Hastings method is explained. At the end of the course students should be familiar with common techniques in rendering and find their way around the c ## Транскрипт ### [] now let's proceed to vertex connection ### Vertex Connection and Merging [0:01] and merging by Aaliyah area and colleagues so what he proposes to do is that we conditionally accept this path the vertex next to XS but we pretend that we indeed have to hit what this basically means is that we have a biased connection something that didn't really happen but we pretend that it did and we have this art that's the merging radius ### The Merging Radius [0:24] so what this means is that on the left side this X s star I would put on X s instead if it is close by and by close by we mean that it is in a circle that is of radius R okay but what does this give to me because this is a bias technique if you add one more trick and this trick would be making our decay over time so this would shrink and shrink and eventually it would get to an epsilon value and that to something that's very close to zero in an infinite amount of time so the bias would disappear from the renderer in time that's quite remarkable I'll tell you in a second why some results with the vertex connection and merging technique you can see that it can render this difficult SDS light transport situation so this is indeed a historical moment in global illumination why because this kind of unifies biased and unbiased photorealistic rendering and that's huge because biased and unbiased rendering was the two biggest schools in photorealistic image synthesis there were the unbiased guys who were there rigorous scientific let's sample all the light paths and let's not cut corners type of people and there were the biased guys who said that okay let's cut corners because this thing takes forever so let's use some optimization techniques and what vertex merging gives you is essentially an algorithm that starts out biased but it has less and less bias as time goes by eventually ending up as an unbiased technique so this is a historical moment that unifies unbiased and biased photorealistic rendering wonderful piece of work now a comparison first bi-directional path tracing then progressive photo mapping and vertex connection and merging make sure to check out the paper here onwards two ### Paths Based Regularization [2:35] paths based regularization this is a work of Anton Kaplan and colleagues is a super smart guy at the kit and this is essentially a generalization of vertex connection and merging what is happening is essentially not spatial but angular regularization what does this mean what we're looking for is connecting the diffuse vertex to the specular with VCM what you would do is you would continue the light path from the light source and you would hit a point that is nearby this next specular vertex and you would set this tolerance this radius this merging radius R and if it's inside then you accept the light path now this you can call spatial regularization what Anton is proposing is angular ### Angular Regularization [3:25] regularization so you would say that you will take a tolerance value in terms of outgoing values and this intuition is slightly different because what this essentially means is that we have Delta distributions for specular reflections but we start out with a large angular tolerance and this means that the specular inter reflections will be treated as if they were diffuse so the mirror will show up as if it were a completely white or some colored wall and then it will slowly converge to being a mirror you can imagine this distribution as what you see in the right side that you have the blue the diffuse ish brdf and you put your two fingers on the sides of this and you start pushing them together and this push happens in time so as time goes by we go from the blue to the orange to the green and we would even squeeze this green more and more until it gets to be a delta distribution so over time mirrors are going to be mirrors but in the mean time we will be able to render SDS light paths brilliant piece of work and the comparison to other algorithms what you should be looking out for is path tracing with regularization on the right and this is the only technique that can render this eg the Euro graphics logo reflected in the mirror