Scale Ai, Inc.

Akshat Bubna

Nathaniel John Herman

Samuel Jacob Clearman

Chiao-Lun Cheng

Leigh Marie Braswell

Steven Hao

Evan Moss

Shariq Shahab Hashme

Liren Tu

Alexandr Wang

Jeffrey Zhihong Li

Guido Leandro Maliandi

James Lennon

One embodiment of the present invention sets forth a technique for evaluating labeled data. The technique includes selecting, from a set of labels for a data sample, a subset of the labels representing non-outliers in a distribution of values in the set of labels. The technique also includes aggregating the subset of the labels into a benchmark for the data sample. The technique further includes generating, based on a comparison between the benchmark and an additional label, a benchmark score associated with the data sample, and generating a set of performance metrics for labeling the data sample based on the benchmark score.

Automatic benchmarking of labeling tasks

One embodiment provides a user interface (UI) that permits users to select how point cloud colorings determined from multiple data sources are blended together in a rendering of a point cloud. The data sources may include photographic, label, and/or LIDAR intensity data. To improve frame rates, an aggregated point cloud may be generated using a spatial hash of a large set of points and sampling of each hash bucket based on the number of points therein and a user-configurable density. Sizes of points in the point cloud may decrease proportionally to distance from a viewer, but increase based on an activation function that enlarges points greater than a threshold distance from the viewer. In addition, luminance statistics for sub-regions of photographic data and dominant colors determined from photographic data may be used to automatically determine color properties to apply to a point cloud coloring.

Visualization techniques for data labeling

Growing community of inventors

San Francisco, CA, United States of America

Akshat Bubna