Amazon Technologies, Inc.

Alex Williams

Li Erran Li

Weifeng Chen

Kumar Hemachandra Chellapilla

Xiong Zhou

Patrick Guy Haffner

Min Bai

Subramanian Chidambaram

Matthew Alan Lease

Jonathan Buck

Techniques for 3D point cloud labeling are described. An example of labeling includes initializing a point-cloud environment according to a configuration; loading of point-cloud data into a memory buffer of a device, wherein the point-cloud data is compatible with a virtual reality (VR) environment and a non-VR environment; drawing of at least the loaded point-cloud data into a point-cloud environment of the device; receiving user input in a task user interface of the device; and in response to the user input, performing an operation to one or more of a bounding box and quality of a label.

3D point-cloud labeling

This disclosure describes a system that models the complexity of image labeling tasks utilizing image data, labeling instructions, and label requirements. A labeling service of a service provider network includes an application that determines a task complexity value based on a data complexity value, a cognitive complexity value, and a product complexity value. The task complexity value is used to predict or estimate needed resources for an image labeling task for labeling image data, e.g., the time and effort needed to label (annotate) the image data. Once the needed resources are estimated, associated costs may also be estimated. The needed resources and associated costs may be provided to a user that submitted the image data, who may then provide an indication with respect to proceeding with the image labeling task.

Automated estimation of needed resources related to labeling a labeling service within a service provider network

This disclosure describes automatically identifying videos of a video dataset having high-content similarity and grouping the videos together with one or more possible tags for the videos for consideration by annotators. A labeling service of a service provider network receives (i) a dataset of unlabeled videos and (ii) category tags for labeling the videos from a client device associated with a user. A video data modeling engine analyzes the dataset to provide a set of ranked unlabeled videos from the dataset, wherein the set of ranked unlabeled videos are ranked according to confidence scores with respect to at least one tag. Based at least in part on the confidence scores, at least some of the unlabeled videos are labeled with the at least one tag to provide a dataset of labeled videos. The dataset of labeled videos is provided to a database and/or the user.

Automated identification of labels for videos with a labeling service within a service provider network

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Johnson City, TN, United States of America

Alex Williams