Mapbox, Inc.

Aleksandr Buslaev

Henadzi Klimuk

Roman Kuznetsov

A mobile computing device receives an image from a camera physically located within a vehicle. The mobile computing device inputs the image into a convolutional model that generates a set of object detections and a set of segmented environment blocks in the image. The convolutional model includes subsets of encoding and decoding layers, as well as parameters associated with the layers. The convolutional model relates the image and parameters to the sets of object detections and segmented environment blocks. A server that stores object detections and segmented environment blocks is updated with the sets of object detections and segmented environment blocks detected in the image.

Low power consumption deep neural network for simultaneous object detection and semantic segmentation in images on a mobile computing device

AR elements are occluded in video image frames. A depth map is determined for an image frame of a video received from a video capture device. An AR graphical element for overlaying over the image frame is received. An element distance for AR graphical elements relative to a position of a user of the video capture device (e.g., the geographic position of the video capture device) is also received. Based on the depth map for the image frame, a pixel distance is determined for each pixel in the image frame. The pixel distances of the pixels in the image frame are compared to the element distance, and in response to a pixel distance for a given pixel being less than the element distance, the pixel of the image frame is displayed rather than a corresponding pixel of the AR graphical element.

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Minsk, Belarus

Aleksandr Buslaev