Google Releases TensorFlow 3D to Advance Spatial Scene Understanding
Google launches TensorFlow 3D, a new library designed to help researchers process 3D sensor data for applications like autonomous vehicles and augmented reality. The open-source toolkit provides essential models and pipelines to lower the barrier to entry for 3D deep learning.
Google introduces TensorFlow 3D (TF 3D), a highly modular and efficient library that brings 3D deep learning capabilities directly into the TensorFlow ecosystem. This new release aims to meet the growing need for advanced scene understanding technology driven by the increasing ubiquity of 3D sensors like LiDAR, depth-sensing cameras, and radar in autonomous cars, robots, and mobile devices.
The TF 3D library provides researchers with a comprehensive set of popular operations, loss functions, data processing tools, and metrics required to develop, train, and deploy state-of-the-art 3D models. It includes ready-to-use training and evaluation pipelines for 3D semantic segmentation, 3D object detection, and 3D instance segmentation, while also supporting distributed training across multiple machines.
To ensure broad usability, TF 3D offers a unified dataset specification that currently supports major datasets like Waymo Open, ScanNet, and Rio out of the box. Users can easily convert other popular datasets such as NuScenes and KITTI into the compatible format, allowing them to leverage the library for everything from rapid prototyping to deploying real-time 3D inference systems.