Systems and methods are provided for estimating head pose data from a sequence of depth images of a human subject, and processing the data to generate a continuous estimate of the head pose in a 3-dimensional (3D) space, and to generate a 3D head model for display and further use. The subject is provided instructions to rotate their head in a first direction until a threshold angle of rotation is reached and then are provided instructions to rotate their head in a second direction. The depth camera provides a sequence of captured images which are processed to extract head meshes. After capture is complete the head meshes are merged to generate a 3D model of the subject's head.

According to one embodiment, an information processing apparatus includes processing circuitry. The processing circuitry acquires a measurement value including an acceleration, corrects the measurement value to reduce a drift in the measurement value, acquires a first image and a second image, detects feature points in the first and the second images, identify a corresponding point among the feature points in the second image, which corresponds to one of the feature points in the first image, and estimate a relative displacement of a moving object between a time at which the first image is taken and a time at which the second image is taken, based on the corrected measurement value, the feature point in the first image and the corresponding point in the second image.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures plurality of dimensions of the home based upon processor analysis of the LIDAR data; builds a 3D model of the home based upon the measured plurality of dimensions; and displays a representation of the 3D model by visually navigating through the 3D model.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using surfels for vehicle localization. One of the methods includes obtaining surfel data comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment, and each surfel has associated data that comprises a stability measure, wherein the stability measure characterizes a permanence of a surface represented by the surfel; obtaining sensor data for a plurality of locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining a plurality of high-stability surfels from the plurality of surfels in the surfel data; and determining a location in the environment of the first vehicle using the plurality of selected high-stability surfels and the sensor data.

Method for decomposing a complexly shaped object in a data set, such as a geobody (31) in a seismic data volume, into component objects more representative of the true connectivity state of the system represented by the data set. The geobody is decomposed using a basis set of eigenvectors (33) of a connectivity matrix (32) describing the state of connectivity between voxels in the geobody. Lineal subspaces of the geobody in eigenvector space are associated with likely component objects (34), either by a human interpreter (342) cross plotting (341) two or more eigenvectors, or in an automated manner in which a computer algorithm (344) detects the lineal sub-spaces and the clusters within them.

A method for optimizing initial discriminating information extracted from a two-dimensional image of the patient dental arches, referred to as “acquired image”, by a three-dimensional digital reference model of at least one portion of a patient arch, the method including the steps: C1. assessing quality of the initial discriminating information and quality threshold, filtering to keep only the initial discriminating information that has quality higher than the quality threshold, and defining “the discriminating information to be tested” as the initial discriminating information selected; C2. testing consistency between the discriminating information to be tested and reference model; C3. assessing test result and, in accordance with the assessment: adding discriminating information that was not kept to the discriminating information to be tested and/or deleting discriminating information from the discriminating information to be tested, and then returning to step C2. or; defining the optimal discriminating information as the discriminating information to be tested.

Disclosed is a smart mirror system for hairstyling using virtual reality, the smart mirror system including: a mirror display provided with a camera and an angle adjusting means, the mirror display being provided on a wall of a hair salon; a chair rotatably provided in front of the mirror display; and a smart device for being mirrored with the mirror display, such that a user uses the mirror display by manipulating the smart device, wherein the smart device is configured to allow hairstyles that match sex and an age group provided by using an app or a server or hairstyles of celebrities provided by Internet search to be displayed on the mirror display by mirroring; and when one of the hairstyles is selected, a selected hairstyle is applied to an image of the user displayed on the mirror display, thereby being three-dimensionally displayed in response to a user's movement.

Systems and methods are disclosed for generating a 3D computer model of an eyewear product, using a computer system, the method including obtaining an inventory comprising a plurality of product frames; scanning a user's anatomy; extracting measurements of the user's anatomy; obtaining a first model of a contour and/or surface of the user's anatomy, based on the extracted measurements of the user's anatomy; identifying, based on the contour and/or the surface of the user's anatomy, a first product frame among the plurality of product frames; determining adjustments to the first product frame based on the contour and/or the surface of the user's anatomy; generating a second model rendering comprising the adjusted first product frame matching the contours and/or the surface of the user's anatomy.

The present 3D-Flow OPRA is a revolutionary electronic instrument for multiple applications: advancing science, saving lives, finding and tracking fast moving objects, etc. It allows to build a flexible, scalable, technology-independent, cost-effective powerful tool to uncover the unknown and to confirm or exclude the existence of a subatomic particle predicted by theoretical physicists. When used for Medical Imaging applications the 3D-Flow OPRA allows to accurately measure minimum abnormal biological processes of diseases at an early curable stage such as cancer in a 3D-CBS (3-D Complete Body Screening), improving diagnosis and prognosis to maximize reduction of premature deaths and minimize cost per each life saved. It is capable of executing programmable pattern recognition algorithms in real-time of multidimensional objects by analyzing in a single crate of electronics of 36 cubic cm, ALL data arriving at ultra-high speed from a matrix of thousands of transducers at over 20 TB/sec with zero dead-time. Both instruments, the 3D-Flow OPRA and the 3D-CBS can benefit from the additional ER/DSU invention also described in this non-provisional patent application, which allows to record real data from detectors and replay them to the 3D-Flow OPRA and 3D-CBS systems in a controlled environment to facilitate testing, debugging and measuring the efficiency.

An information processing apparatus (100) extracts, from among a plurality of elements, a first element that falls within a predetermined range from a first reference for determination of success or failure or a second element that falls within a predetermined range from a second reference for determination of a perfection level, on the basis of at least one of a determination result of the success or failure and a determination result of the perfection level. The information processing apparatus (100) extracts a portion corresponding to the first element or the second element from a 3D model moving image that is generated from 3D sensing data of a series of performances of the athlete or a video that is obtained by capturing the performances, and outputs the extracted moving image or the extracted video.