Google Releases TensorFlow 3D to Advance Spatial Scene Understanding
Google introduces TensorFlow 3D, a new open-source library designed to help researchers process 3D sensor data for applications like autonomous driving and augmented reality.
Google unveils 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 demand for advanced scene understanding technology driven by the increasing ubiquity of 3D sensors like Lidar, depth-sensing cameras, and radar in modern devices.
The library provides a comprehensive suite of tools, including popular operations, loss functions, data processing utilities, and metrics required to develop, train, and deploy state-of-the-art 3D models. TF 3D features ready-to-use training and evaluation pipelines for 3D semantic segmentation, 3D object detection, and 3D instance segmentation, with full support for distributed training across multiple machines.
Researchers easily configure standard 3D datasets like Waymo Open, ScanNet, and Rio, while also retaining the flexibility to convert other popular datasets such as NuScenes and Kitti into compatible formats. By reducing barriers to entry, TF 3D empowers developers to prototype new ideas and deploy real-time inference systems for a wide variety of 3D deep learning applications.