Google Releases TensorFlow 3D to Advance Spatial Scene Understanding
Google introduces TensorFlow 3D, a new library designed to make 3D deep learning more accessible to researchers. The tool provides essential operations and pipelines for tasks like 3D object detection and semantic segmentation.
Google releases TensorFlow 3D (TF 3D), a highly modular and efficient library that brings 3D deep learning capabilities into the TensorFlow ecosystem. This new tool addresses the growing need for advanced scene understanding technology required by 3D sensors like Lidar and depth cameras, which power autonomous cars, robots, and augmented reality experiences on mobile devices.
The library provides a comprehensive set of operations, loss functions, data processing tools, and metrics that enable researchers to develop, train, and deploy state-of-the-art 3D models. TF 3D includes training and evaluation pipelines for 3D semantic segmentation, 3D object detection, and 3D instance segmentation, complete with built-in support for distributed training across multiple machines.
Researchers can easily work with popular 3D datasets through the unified dataset specification, with native support for Waymo Open, ScanNet, and Rio datasets. Users also have the flexibility to convert other common datasets like NuScenes and Kitti into the compatible format, allowing them to prototype new ideas or deploy real-time inference systems for a wide variety of 3D deep learning applications.