AI & Deep Learning
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Date: February 12, 2020
Time: 10:00am – 11:00am PT
Duration: 1 hour

Join this webinar to learn how to curate state-of-the-art 3D deep learning architectures for research.

Research in 3D computer vision and AI have been on the rise, but researchers have lacked good utilities to make 3D models ready for deep learning. To bridge the divide, NVIDIA released Kaolin to accelerate 3D deep learning research and move 3D models into the realm of neural networks.

Kaolin is a PyTorch library for accelerating 3D deep learning research with efficient implementations of differentiable 3D modules for use in deep learning systems. It also mitigates the need to write wasteful boilerplate code while packaging together several differentiable graphics modules, including rendering, lighting, shading, and view warping. The functionality loads and preprocesses popular 3D datasets and native functions to manipulate meshes, point clouds, signed distance, and voxel grids. Kaolin supports an array of functions and metrics for seamless evaluation and visualization to render the 3D results.

By attending this webinar, you'll learn:
  • How researchers use Kaolin to accelerate 3D deep learning research
  • An overview of 3D deep learning tasks, such as 3D classification, 3D segmentation, single-image 3D reconstruction, and differentiable rendering
  • How to train a neural network for a custom task, like avoiding collisions with JetBot 
  • Applications and challenges of using Kaolin to curate state-of-the-art 3D renderings
Join us after the presentation for a live Q&A session.



You will receive an email with instructions on how to join the webinar shortly.

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.


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Krishna Murthy Jatavallabhula

PhD Student, Mila, Université de Montréal

Krishna is a PhD student at Mila, Université de Montréal, and at the Robotics and Embodied AI Lab (REAL), advised by Liam Paull. His interests are diverse, predominantly along the intersections of robotics, computer vision, deep learning, and computer graphics. Krishna spent the summer of 2019 with an amazing research group led by Sanja Fidler at NVIDIA, Toronto, where he led the development of Kaolin, a 3D deep learning library for PyTorch. Prior to joining his PhD in 2018, he pursued a master’s degree in computer science at IIIT Hyderabad, India, where he was advised by K. Madhava Krishna.

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Webinar: Description here

Date & Time: Wednesday, April 22, 2018