Systems and methods are provided for an advanced stereoscopic 3D rendering system to solve several of these 3D rendering issues and work independently of different game engines. For example, the system can include a stereoscopic render mechanism to apply post-processing effects to OpenGL ES applications. The advanced stereoscopic 3D rendering system can be added as an interception layer between the game engine and the display screen of the user. This interception layer can be integrated with many different game engines to create a 3D view for various user device models, thus removing the need for image generators at the user device to create 3D images. The 3D images can be created by the advanced stereoscopic 3D rendering system for viewing by the end user and seemingly incorporated with the game or other software application.

Systems and methods are provided for an advanced stereoscopic 3D rendering system to solve several of these 3D rendering issues and work independently of different game engines. For example, the system can include a stereoscopic render mechanism to apply post-processing effects to OpenGL ES applications. The advanced stereoscopic 3D rendering system can be added as an interception layer between the game engine and the display screen of the user. This interception layer can be integrated with many different game engines to create a 3D view for various user device models, thus removing the need for image generators at the user device to create 3D images. The 3D images can be created by the advanced stereoscopic 3D rendering system for viewing by the end user and seemingly incorporated with the game or other software application.

A method of calibrating cameras used to collect images to form an omni-stereo image is disclosed. The method may comprise determining intrinsic and extrinsic camera parameters for each of a plurality of left eye cameras and right eye cameras arranged along a viewing circle or ellipse and angled tangentially with respect to the viewing circle or ellipse; categorizing left-right pairs of the plurality of left eye cameras and the plurality of right eye cameras into at least a first category, a second category or a third category; aligning the left-right pairs of cameras that fall into the first category; aligning the left-right pairs of cameras that fall into the second category; and aligning the left-right pairs of cameras that fall into the third category by using extrinsic parameters of the left-right pairs that fall into the first category, and of the left-right pairs that fall into the second category.

A method of calibrating cameras used to collect images to form an omni-stereo image is disclosed. The method may comprise determining intrinsic and extrinsic camera parameters for each of a plurality of left eye cameras and right eye cameras arranged along a viewing circle or ellipse and angled tangentially with respect to the viewing circle or ellipse; categorizing left-right pairs of the plurality of left eye cameras and the plurality of right eye cameras into at least a first category, a second category or a third category; aligning the left-right pairs of cameras that fall into the first category; aligning the left-right pairs of cameras that fall into the second category; and aligning the left-right pairs of cameras that fall into the third category by using extrinsic parameters of the left-right pairs that fall into the first category, and of the left-right pairs that fall into the second category.

Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; and capturing one or more images of the structure from the pose.

A method for controlling a laser profiler, the laser profiler being configured for generating a laser line on a surface to be inspected, the method comprising: receiving an image of the laser line; determining an actual intensity of the laser line; calculating an amplification factor for the laser line based on the actual intensity of the laser line, a target intensity for the laser line, a power of the laser, a camera gain of the camera and an exposure time of the laser line on the surface to be inspected, the amplification factor allowing the actual intensity of the laser line to reach the target intensity while minimizing the power of the laser; and based on the calculated amplification factor, adjusting at least one parameter of the laser profiler so that the actual intensity of the laser line corresponds to the target intensity.

A method for controlling a laser profiler, the laser profiler being configured for generating a laser line on a surface to be inspected, the method comprising: receiving an image of the laser line; determining an actual intensity of the laser line; calculating an amplification factor for the laser line based on the actual intensity of the laser line, a target intensity for the laser line, a power of the laser, a camera gain of the camera and an exposure time of the laser line on the surface to be inspected, the amplification factor allowing the actual intensity of the laser line to reach the target intensity while minimizing the power of the laser; and based on the calculated amplification factor, adjusting at least one parameter of the laser profiler so that the actual intensity of the laser line corresponds to the target intensity.

Context-sensitive remote controls for use with electronic devices (e.g., eyewear device). The electronic device is configured to perform activities (e.g., email, painting, navigation, gaming). The context-sensitive remote control includes a display having a display area, a display driver coupled to the display, and a transceiver. The remote control additionally includes memory that stores controller layout configurations for display in the display area of the display by the display driver. A processor in the context-sensitive remote control is configured to establish, via the transceiver, communication with an electronic device, detect an activity currently being performed by the electronic device, select one of the controller layout configurations responsive to the detected activity, and present, via the display driver, the selected controller layout configuration in the display area of the display.

Example systems, devices, media, and methods are described for capturing still images in response to hand gestures detected by an eyewear device that is capturing frames of video data with its camera system. A localization system determines the eyewear location relative to the physical environment. An image processing system detects a hand shape in the video data and determines whether the detected hand shape matches a border gesture or a shutter gesture. In response to a border gesture, the system establishes a border that defines the still image to be captured. In response to a shutter gesture, the system captures a still image from the frames of video data. The system determines a shutter gesture location relative to the physical environment. The captured still image is presented on the display at or near the shutter gesture location, such that the still image appears anchored relative to the physical environment. The captured still image is viewable by other devices that are using the image capture system.

A method includes receiving, from a multiscopic image capture system, a plurality of images depicting a scene. The method includes determining, by application of a neural network based on the plurality of images, a disparity map of the scene. The neural network includes a plurality of layers, and the layers include a rectification layer. The method include determining a matching error of the disparity map based on differences between corresponding pixels of two or more images associated with the disparity map. The method includes back-propagating the matching error to the rectification layer of the neural network. Back-propagating the matching error includes updating one or more weights applied to the rectification layer.