Abstract: The subject disclosure relates to techniques for improving map labeling and in particular, for automating process of semantically labeling maps used for autonomous vehicle (AV) navigation. In some aspects, a process of the disclosed technology includes steps for receiving a three-dimensional (3D) Light Detection and Ranging (LiDAR) map, processing the 3D LiDAR map to produce a 2D map, rasterizing the 2D map into a plurality of map tiles, and providing one or more of the map tiles to a machine-learning (ML) labeler, wherein the ML labeler is configured to identify and label image features in at least one of the one or more map tiles. Systems and machine-readable media are also provided.

Abstract: The subject disclosure relates to techniques for improving map labeling and in particular, for automating process of semantically labeling maps used for autonomous vehicle (AV) navigation. In some aspects, a process of the disclosed technology includes steps for receiving a three-dimensional (3D) Light Detection and Ranging (LiDAR) map, processing the 3D LiDAR map to produce a 2D map, rasterizing the 2D map into a plurality of map tiles, and providing one or more of the map tiles to a machine-learning (ML) labeler, wherein the ML labeler is configured to identify and label image features in at least one of the one or more map tiles. Systems and machine-readable media are also provided.

Abstract: A method is provided for processing image data using a computer system. This method includes: receiving a plurality of image descriptors, each of the image descriptors representing a unique visual characteristic; receiving image data representative of a primary image; processing the image data to select a first subset of the image descriptors that represent a plurality of visual characteristics of the primary image; receiving an image dataset representative of a plurality of secondary images; and processing the image dataset based on the first subset of the image descriptors to determine which of the secondary images are visually similar to the primary image.

Abstract: Various arrangements for customizing a configuration of a mobile device are presented. The mobile device may collect proximity data. The mobile device may determine that a user has gripped the mobile device based on the proximity data. A finger length of the user may be determined using the proximity data. Configuration of the mobile device may be customized at least partially based on the determined finger length of the user.

Abstract: Various arrangements for customizing a configuration of a mobile device are presented. The mobile device may collect proximity data. The mobile device may determine that a user has gripped the mobile device based on the proximity data. A finger length of the user may be determined using the proximity data. Configuration of the mobile device may be customized at least partially based on the determined finger length of the user.

Abstract: Various arrangements for customizing a configuration of a mobile device are presented. The mobile device may collect proximity data. The mobile device may determine that a user has gripped the mobile device based on the proximity data. A finger length of the user may be determined using the proximity data. Configuration of the mobile device may be customized at least partially based on the determined finger length of the user.

Abstract: Various arrangements for customizing a configuration of a mobile device are presented. The mobile device may collect proximity data. The mobile device may determine that a user has gripped the mobile device based on the proximity data. A finger length of the user may be determined using the proximity data. Configuration of the mobile device may be customized at least partially based on the determined finger length of the user.