An image generation device includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: select a second face image from a plurality of face images stored in advance based on directions of faces included in the plurality of face images and a direction of a face included in an input first face image; deform the second face image based on feature points of the face included in the first face image and feature points of a face included in the second face image such that a face region of the second face image matches a face region of the first face image; and generate a third face image in which the face region of the first face image is synthesized with a region other than the face region of the deformed second face image.

Systems and methods described herein relate to registering three-dimensional (3D) data with two-dimensional (2D) image data. One embodiment receives 3D data from one or more sensors and 2D image data from one or more cameras; identifies a 3D segment in the 3D data and associates it with an object; classifies pixels in the 2D image data; determines a speed and a heading for the object; and registers the 3D segment with a portion of the classified pixels by either (1) shifting the 3D segment to a position that, based on the associated object's speed and heading, corresponds to a 2D image data capture time and projecting the time-shifted 3D segment onto 2D image space; or (2) projecting the 3D segment onto 2D image space and shifting the projected 3D segment to a position that, based on the associated object's speed and heading, corresponds to a 2D image data capture time.

Systems and methods described herein relate to registering three-dimensional (3D) data with two-dimensional (2D) image data. One embodiment receives 3D data from one or more sensors and 2D image data from one or more cameras; identifies a 3D segment in the 3D data and associates it with an object; identifies 2D boundary information for the object; determines a speed and a heading for the object; and registers the 3D segment with the 2D boundary information by adjusting the relative positions of the 3D segment and the 2D boundary information based on the speed and heading of the object and matching, in 3D space, the 3D segment with projected 2D boundary information.

The systems may include superimposing a canvas layer having a pixel system over the digital map; aligning the coordinate system of the digital map with the pixel system of the canvas layer; obtaining a location coordinate of the coordinate system to each site of interest of a plurality of sites of interest, wherein the location coordinate is associated with a location of the site of interest on the digital map; associating the location coordinate for the site of interest with a pixel in the pixel system; and creating a marker on the canvas layer on the pixel associated with the location coordinate for the site of interest.

Systems and methods for aligning digital image datasets to a computer model of a structure. The system receives a plurality of reference images from an input image dataset and identifies common ground control points (GCPs) in the reference images. The system then calculates virtual three-dimensional (3D) coordinates of the measured GCPs. Next, the system calculates and projects two-dimensional (2D) image coordinates of the virtual 3D coordinates into all of the images. Finally, using the projected 2D image coordinates, the system performs spatial resection of all of the images in order to rapidly align all of the images.

A method for rendering perspective adjusted views of a virtual object in a real world environment is provided. A registration code is generated for a first device and includes a static portion for device identification and a dynamic portion for a location and orientation of the first device. The dynamic portion of the registration code changes based on time passage and movement of the first device. A distance and orientation of the first device is determined with respect to a second device based on a location and orientation of the second device at a particular time and the registration code, which is captured by the second device at the particular time. The second device captures the registration code via the first device or a different device. A display of a virtual object is perspective adjusted based on the distance and orientation of the devices.

A computer-implemented method for learning an autoencoder notably is provided. The method comprises providing a dataset of images. Each image includes a respective object representation. The method also comprises learning the autoencoder based on the dataset. The learning includes minimization of a reconstruction loss. The reconstruction loss includes a term that penalizes a distance for each respective image. The penalized distance is between the result of applying the autoencoder to the respective image and the set of results of applying at least part of a group of transformations to the object representation of the respective image. Such a method provides an improved solution to learn an autoencoder.

A method and an apparatus for generating a set of processed images of an object from overlapping raw images are provided. The raw images are aligned so that pixels representing a same part of the picture object in different raw images are aligned forming a respective pixel stack for each pictured part of the object. At least one mask image is then generated by a maximum intensity projection through the pixel stacks. The set of processed images is then generated from the raw images using the at least one mask image.

When three-dimensional audio is produced by using headphones particular HRTF-filters are used the sound for left and right channels of the headphone. As the morphology of every ear is different, it is beneficial to have HRTF-filters particularly designed for the user of headphones. Such filters may be produced deriving ear geometry from a plurality of images taken with an ordinary camera, detecting necessary features from images and fitting said features to a model that has been produced from accurately scanned ears comprising representative values for different sizes and shapes. Taken images are sent to a server that performs the necessary computations and submits the data further or produces the requested filter.

A system for calibrating at least one camera and a light detection and ranging (LiDAR) sensor includes one or more processors and a memory in communication with the one or more processors that stores an initial calibration module and a user calibration module. The initial calibration module includes instructions that cause the one or more processors to obtain a camera image from the at least one camera, determine when the camera image includes at least one object having at least one edge, obtain a three-dimensional point cloud image from the LiDAR sensor that includes the at least one object having at least one edge and generate a combined image, that includes at least portions of the camera image and at least portions of the three-dimensional point cloud image. The user calibration module includes instructions that cause the one or more processors to display the combined image on a display, receive at least one input from a user interface, and adjust a calibration parameter of at least one of the LiDAR sensor and the at least one camera in response to the one or more inputs from the user interface.