Systems and methods are disclosed herein for monitoring a location of a client device associated with a transportation service and generating augmented reality images for display on the client device. The systems and methods use sensor data from the client device and a device localization process to monitor the location of the client device by comparing renderings of images captured by the client device to renderings of the vicinity of the pickup location. The systems and methods determine navigation instructions from the user's current location to the pickup location and select one or more augmented reality elements associated with the navigation instructions and/or landmarks along the route to the pickup location. The systems and methods instruct the client device to overlay the selected augmented reality elements on a video feed of the client device.

An electronic device with a camera obtains, with the camera, one or more images of a scene. The electronic device detects a respective feature within the scene. In accordance with a determination that a first mode is active on the device, the electronic device provides a first audible description of the scene. The first audible description provides information indicating a size and/or position of the respective feature relative to a first set of divisions applied to the one or more images of the scene. In accordance with a determination that the first mode is not active on the device, the electronic device provides a second audible description of the plurality of objects. The second audible description is distinct from the first audible description and does not include the information indicating the size and/or position of the respective feature relative to the first set of divisions.

Placement of a face depicted within a video may be determined. One or more stabilization options for the video may be obtained. Stabilization option(s) may include angle stabilization option, a position stabilization option, and/or a size stabilization option. The video may be stabilized based on the placement of the face and the stabilization option(s).

Disclosed are systems and methods for obtaining a scale factor and 3D measurements of objects from a series of 2D images. An object to be measured is selected from a menu of an Augmented Reality (AR) based measurement application being executed by a mobile computing device. Measurement instructions corresponding to the selected object are retrieved and used to generate a series of image capture screens. A series of image capture screens assist the user in positioning the device relative to the object in a plurality of imaging positions to capture the series of 2D images. The images are used to determine one or more scale factors and to build a complete scaled 3D model of the object in virtual 3D space. The 3D model is used to generate one or more measurements of the object.

A system includes a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising displaying a prompt to a user of a mobile device on a display of a mobile device to capture an image representing at least a portion of a mouth of the user using a rear-facing camera of the mobile device, where the rear-facing camera is on an opposite side of the mobile device including the display. The operations further comprise controlling the rear-facing camera to enable the rear-facing camera to capture the image, receiving the image, and outputting, user feedback based on the image, where the user feedback is outputted on the display that is on the opposite side of the mobile device than the rear-facing camera.

A camera system includes an administrator and a series of camera nodes which includes an audio microphone for also including sound in the video data file. The administrator includes a computer server, a wireless transmitter/receiver (transceiver), and machine learning (ML) chips. Each camera node includes a video capturing device or camera, a wireless transmitter/receiver (transceiver), a plurality of ML chips, and a GPS sensor. The ML chips are capable of receiving data in the form of a video file and processing the data to determine if the data includes “actionable” data. The ML chip makes certain inferences from the captured video data. The camera nodes are wirelessly linkable to each other. The communication between the several camera nodes form a “mesh network” wherein the several camera nodes may transmit data to each other, thus propagating the camera nodes with the mesh network with common data, rules, or inferences.

Described are systems and method directed to generation of a dimensionally accurate three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different directions with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model of the body of the user.

A point cloud capture system is provided to detect and correct data density during point cloud generation. The system obtains data points that are distributed within a space and that collectively represent one or more surfaces of an object, scene, or environment. The system computes the different densities with which the data points are distributed in different regions of the space, and presents an interface with a first representation for a first region of the space in which a first subset of the data points are distributed with a first density, and a second representation for a second region of the space in which a second subset of the data points are distributed with a second density.

Various implementations disclosed herein include devices, systems, and methods that generates a three-dimensional (3D) model based on a selected subset of the images and depth data corresponding to each of the images of the subset. For example, an example process may include acquiring sensor data during movement of the device in a physical environment including an object, the sensor data including images of a physical environment captured via a camera on the device, selecting a subset of the images based on assessing the images with respect to motion-based defects based on device motion and depth data, and generating a 3D model of the object based on the selected subset of the images and depth data corresponding to each of the images of the selected subset.

An image processing apparatus includes: an image acquisition unit configured to acquire a photographed image; an editing unit configured to edit the photographed image by executing an action including an application and image processing that is a function of the application; an information acquisition unit configured to acquire photographing information on photographing of the photographed image; a first acquisition unit configured to acquire first history information indicating a history of actions for the photographed image; a second acquisition unit configured to acquire second history information based on at least a history of actions for an image different from the photographed image; and a control unit configured to determine a candidate for an action that is to be executed next on a basis of the first history information and the second history information and perform control to notify the determined candidate.