Facebook Introduces PyTorch3D, a Deep Learning Library!

A couple of days ago, Facebook AI Research team revealed PyTorch3D, a library that will allow developers and researchers to blend 3D objects and deep learning. Moreover, Facebook is making Mesh R-CNN publicly available. Mesh R-CNN was rolled out last year and it can be used to render 3D objects from 2D shapes that assume the form of images of interior spaces.

As a matter of fact, Mesh R-CNN inspired the creation of PyTorch3D and a bunch of 3D work done by FAIR lately, as per FAIR engineer Nikhila Ravi.

Operating in 3D is quite essential when it comes to rendering 3D objects as well as scenes that we usually find in VR/mixed reality. It also comes in handy while dealing with numerous AI challenges that involve autonomous vehicles and robotics.

PyTorch3D is packed with commonly employed 3D operators in addition to loss functions reserved for 3D data and a differentiable mesh renderer for forming 3D objects. Facebook AI researchers also boasted about a number of other perks of PyTorch3D such as CUDA support, differential rendering API etc.

Facebook AI team also added that using PyTorch3D, researchers can easily use these functions with the already available deep learning system in PyTorch. It saves everyone the time that would otherwise be spent on 3D planning research, something that requires loads of expertise just to get started.

PyTorch3D employs meshes, an essential data format for constituting 3D objects, and can utilize a patch tensor which (in simpler terms) is helpful when it comes to batching, a usual process for deep learning research.

PyTorch3D comes after PyRobot robotics framework that was rolled out last year, and FAIR 3D research that draws out characters from real-life videos.

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