3D deep learning-modellen bouwen met PyTorch3D Building 3D deep learning models with PyTorch3D

Ontdek hoe het werkt in deze video van PyTorch3D-medebedenker en software engineer Nikhila Ravi: https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/ Zoals Torchvision en Detectron2 sterk geoptimaliseerde libraries bieden voor 2D computer vision, biedt PyTorch3D mogelijkheden voor 3D-data. Onze open source library voor 3D deep learning ondersteunt eenvoudig batchen van heterogene meshes en point clouds, geoptimaliseerde implementaties van veelgebruikte 3D-operatoren zoals Chamfer Loss en Graph Conv, en een modulaire, differentieerbare renderer voor point clouds en meshes. Bij Facebook gebruiken we PyTorch3D al voor onderzoeksprojecten zoals Mesh R-CNN en SynSin.

Learn more about how it works in this video by PyTorch3D co-creator and software engineer Nikhila Ravi: https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/ In the same way that Torchvision and Detectron2 offer highly optimized libraries for 2D computer vision, PyTorch3D offers capabilities that support 3D data. Our open source library for 3D deep learning includes support for easy batching of heterogeneous meshes and point clouds, optimized implementations of common 3D operators such as Chamfer Loss and Graph Conv, as well as a modular, differentiable renderer for point clouds and meshes. We’re already using PyTorch3D at Facebook for research projects such as Mesh R-CNN and SynSin.