CGLab (since 2016.09) focuses on conducting research on photorealistic rendering, which includes a variety of optimization techniques for ray tracing. The main applications of photorealistic rendering are CG movies, animations, 3D games and immersive technology (AR and VR).
While implementing a quick toy example of Crane and Sawhney's really great Monte Carlo Geometry Processing paper, the question arose about whether a quick function I grabbed from The Internet to equally distribute points on a sphere was correct or not. Since it's absolutely the crux of the method, this is an important question! This notebook performs a rather unscientific check for equal distribution of points on the surface of a sphere. It uses the first algorithm from MathWorld: Sphere Point Picking. Foll
GPUs are designed to do many things well, but drawing transparent 3D objects is not one of them. Opacity doesn't commute so that the order in which you draw surfaces makes a big difference. Of course simple additive blending does commute, but it's not really what we think of as "transparent objects". The simplest way to draw transparent objects is from back to front via the painter's algorithm. In this approach we sort geometry and draw only from back to front. This requires sorting triangles, which, in add
Beginning November 13th, 2020, we will no longer accept account passwords when authenticating with the GitHub REST API. In the future, we will similarly no longer accept account passwords when authenticating Git operations.
This page contains list of mathematical Theorems which are at the same time (a) great, (b) easy to understand, and (c) published in the 21st century. See here for more details about these criteria. Click on any theorem to see the exact formulation, or click here for the formulations of all theorems. You can also…
Let’s imagine a hypothetical situation. There’s an infection going round, and we want to predict the future severity of someone’s illness. There is a test that offers a good prediction. Let’s say the outcome of the test has a correlation of 0.78 with the patient's severity of infection. The problem with the test is that…
Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most…
If you build or maintain software, you’re familiar with GitHub. Millions of developers rely on the massive code repository for everything from source code
These are articles about the techniques I develop and lessons I learnt while toying or working with computer graphics. Most of it is self-taught and there's lots of reinventing the wheel (which I recommend) but also some innovative and new discoveries that often times are not documented anywhere else (and if any of this content becomes part of your paper or the center of your PhD thesis, I feel it'd be fair to mention this website).
This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.
In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.
Pytorch Geometric tutorial part starts at -- 0:33:30
Details on:
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Update
* Graph Pooling
R. Hanocka, G. Metzer, R. Giryes, und D. Cohen-Or. (2020)cite arxiv:2005.11084Comment: SIGGRAPH 2020; Project page: https://ranahanocka.github.io/point2mesh/.