Overview
Toyota Research Institute’s mission is to build a new approach to mobility and pioneering the technologies that drive its future.
The Problem
Large volumes of data, but limited ability to label this data
Led by Adrien Gaidon, TRI’s Machine Learning team found itself with large volumes of data, but limited ability to label this data.
The need for large volumes of annotated data was obvious, but with a commitment to safety being non-negotiable, the TRI team did not want to trade off quality for quantity.
To support the research teams, David Garber and Ashmi Wadhwani were tasked with managing the data annotation pipeline, data infrastructure and machine learning infrastructure.
Very quickly, they recognized the need for a labeling provider they could rely on long term to meet the ever-changing needs of TRI’s researchers.
The Solution
Scale’s Sensor Fusion Cuboids, Sensor Fusion Segmentation and Semantic Segmentation
TRI looked into various solutions for its data annotation problem, but made Scale its main provider. This close working relationship led to TRI participating in the private beta of Sensor Fusion Segmentation, a challenging annotation type in which every point in a 3D point cloud needs to be painted.
The Result
Leveraging latest annotations and features
TRI will continue to leverage Scale's latest annotations and features, such as Sensor Fusion Segmentation.
