Rendering is at the heart of computer graphics, transforming 3D models into 2D images. This is also a common way to bridge between 3D scene properties and 2D image pixels. However, traditional rendering engines cannot make differentials, so they cannot be incorporated into the deep learning work pipeline. PyTorch3D has built-in modular differential renderer that can handle differential3 data.
Facebook recently opened up PyTorch’s library of functions for 3D deep learning, PyTorch3D, a highly modular and optimized library with unique features designed to simplify 3D deep learning through PyTorch. PyTorch3D provides a common set of 3D operators and fast and differential loss functions for 3D data, as well as a modular differential rendering API. With these features, researchers can immediately import these functions into the most advanced deep learning system.
Researchers and engineers can use PyTorch3D for a variety of 3D deep learning studies, whether it’s 3D refactoring, cluster adjustment, or even 3D reasoning, and improve disrecognition tasks in two-dimensional space.
The cognition of three-dimensional space plays an important role in the interaction between artificial intelligence and the real world. For example, robots navigate in physical space, improve the virtual reality experience, and identify objects that are obscured in 2D content. But even Facebook, which has a wealth of deep learning technology, can still be plagued by 3D deep learning problems. Facebook says deep learning technology is less used in 3D scenarios because of a lack of tools and resources to support the complexity of neural networks combined with 3D data, which requiremore memory and higher computing power, unlike 2D images, which can be represented by the amount of sheets. And many traditional graphical operators are not differential, so the study of 3D deep learning techniques is limited.
To do this, Facebook has built a Library of PyTorch3D Functions to drive 3D deep learning research, and like PyTorch, which provides highly optimized libraries for 2D recognition tasks, PyTorch3D optimizes training and reasoning by providing batch processing and support for 3D operators and loss functions. To simplify the complexity of 3D model batching, Facebook created the Meshes format, a heterogeneous grid model data structure designed for deep learning applications for batch processing.
This data structure makes it easy for researchers to quickly transform the underlying grid model data into different views to match operators to the most efficient representation of the data. More importantly, PyTorch3D gives researchers and engineers the flexibility to switch efficiently between different representation views and access different grid properties.
Rendering is at the heart of computer graphics, transforming 3D models into 2D images. This is also a common way to bridge between 3D scene properties and 2D image pixels. However, traditional rendering engines cannot make differentials, so they cannot be incorporated into the deep learning work pipeline. So Facebook has built a highly modular differential renderer in PyTorch3D that can handle differential 3D data. The implementation of this feature consists of composable units that allow users to easily extend the renderer to support custom lighting or shadow effects.
Facebook packages these features into toolkits and provides operators, heterogeneous batching capabilities, modular differential rendering APIs, and more to help researchers with complex 3D neural network application skits.
View PyTorch3d documentation: https://pytorch3d.org/docs/why_pytorch3d.htm