![]() Let’s call it kaolin $ conda create -name kaolin python=3.7 Let’s start by creating a Conda environment for everything that we need to install. If you don’t, you might want to read one of my recent articles in which I show you how to install CUDA 11.2. CUDAīefore installing anything, check that you have CUDA version 10.2 or above installed. If you don’t have Anaconda installed, you can follow this article to complete the Anaconda setup. With Anaconda, it’s easy to install multiple versions of Python, and with the use of virtual environments, we can greatly reduce the chances of finding incompatible versions of a library. We can ease our pain so much by using Anaconda. To be able to run the DIB-R tutorial you will need to have: An API to create 3D Checkpoints( kaolin.visualize) and in the future a lot more. The library also includes DIB-R, the differential renderer(kaolin.render), functions to load data from popular 3D datasets like Shapenet, functions to load 3D models in different file formats, like obj and usd(kaolin.io). Nvidia Kaolin Library, a PyTorch API, that supports different 3D representations, like point clouds, mesh, and voxel grids, and functions that allow conversion from one representation to the other( kaolin.ops).Nvidia Omniverse Kaolin App, is an application created by Nvidia to help 3D Deep Learning researchers by providing a visualization tool for 3D datasets, a means of generating synthetic datasets and even comes with the capability of visualizing the 3D outputs generated by a model during training.Nvidia Kaolin is not just about the PyTorch library. So in this tutorial, I am going to show you step by step how to try the DIB-R tutorial, and also I will share with you what I have learned about DIB-R and the field of 3D Deep Learning. And I even confused the differential renderer with the neural network described in the DIB-R paper, that is capable of generating a 3D object from a single 2D photo. Also, I was not even sure why a differential renderer is even needed in the first place. When I first saw the tutorial, I must confess that I didn’t really understand it much. The good news is that we can now try DIB-R first hand because Nvidia has released a PyTorch library part of Nvidia Kaolin which includes DIB-R, the same differential renderer that was used in the DIB-R paper.īut best of all, the library, also includes a tutorial that showcases the capabilities of DIB-R, the differential renderer. This was quite disappointing since I really wanted to try this first-hand. But unfortunately, it was missing the machine learning model that was needed to run that code. When the DIB-R paper was released, back in 2019, it also included source code. To generate 3D objects from a single 2D image. The DIB-R paper introduced an improved differential renderer as a tool to solve one of the most fashionable problems right now in Deep Learning. This was a key paper for 3D Deep Learning from 2019. If you have read my last article on GANverse3D, then you will probably have heard about the DIB-R paper, which I mentioned a few times. ![]()
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