A 3D human face reconstruction method and apparatus, and a server are provided. In some embodiments, the method includes determining feature points on an acquired 2D human face image; determining posture parameters of a human face according to the feature points, and adjusting a posture of a universal 3D human face model according to the posture parameters; determining points on the universal 3D human face model corresponding to the feature points, and adjusting the corresponding points in a sheltered status to obtain a preliminary 3D human face model; and performing deformation adjusting on the preliminary 3D human face model, and performing texture mapping on the deformed 3D human face model to obtain a final 3D human face.

There is provided a method of determining a three-dimensional shape of an insert for insertion into an ear. The method includes receiving image data corresponding to a two-dimensional image of an ear, processing the image data to measure at least one biometric feature of the ear, the at least one biometric feature being indicative of a three-dimensional shape of at least part of the ear, and determining a three-dimensional shape of an insert for insertion into the ear by matching said at least one biometric feature with one of a plurality of pre-stored three-dimensional shapes. Each pre-stored three-dimensional shape corresponds to a respective ear.

A method of automatically generating human-body parameters using machine learning, including the following steps: initializing a converting program, inputs of which are accurate human-body parameters and outputs of which are general body-shape descriptions; inputting several groups of accurate human-body parameters into the converting program, so as to obtain various combinations of general body-shape descriptions, which are to be used as training sets for subsequent steps; carrying out training through machine learning by using the training sets obtained from Step (1), to obtain a mapping relationship between the general body-shape descriptions and parameters of a 3D human body model; recording gender, height, and weight information from a user and the user's responses to a series of preset general descriptive questions about body shape, and using the mapping relationship obtained from Step (3), to output accurate human-body parameters representing an actual human body of the user.

A methods and system for recognizing a three dimensional object on a base are disclosed. A three dimensional image of the object is received as a three-dimensional point cloud having depth data and color data. The base is removed from the three dimensional point cloud to generate a two-dimensional image representing the object. The two-dimensional image is segmented to determine object boundaries of a detected object. Color data from the object is applied to refine segmentation and match the detected object to a reference object data.

This disclosure relates to an apparatus and a method for 3D printing. The 3D object detection apparatus includes one or more processing circuit configured to acquire a robust shape value of a 3D object; match the robust shape value of the 3D object with a robust shape value of a 3D sample in a 3D sample database; determine a robust design value of the 3D object based on a robust design value of the matched 3D sample; and detect the 3D object based on a result of the matching process for the robust shape value and the determined robust design value of the 3D object. With the apparatus and the method for 3D printing according to the disclosure, an owner, a designer and a manufacturer of a 3D model can protect the 3D model from infringing intellectual property.

The present invention discloses a 3D web search engine for crawling, indexing, and searching through 3D models located on the World Wide Web. The user provides a search query in the form of a 3D model to reach for web pages that contain similar 3D models. The search query can be in the form of a 3D model, an image of a 3D model, or a drawing of a 3D model. The user has different options to filter the 3D models of the search results according to their needs or preference.

A method and an apparatus of tracking an eye gaze to determine where a gaze point of a user is located on a display unit of a device, based on a facial pose and a position of an iris center, are provided. The method includes detecting a facial feature in a captured initial image, three-dimensionally modeling the detected facial feature, tracking the three-dimensionally modeled facial feature in consecutively captured images, detecting an iris center in the consecutively captured images, acquiring an eye gaze vector based on the tracked three-dimensionally modeled facial feature and the detected iris center, and acquiring a gaze point on a display unit based on the eye gaze vector.

Methods and systems for developing a sizing system through categorization and selection of prototypes, which can be regarded as the most appropriate fit model, is described. Once categorized and prototypes are selected, recommendations for the sizing of a target body part may be issued.

Described herein is a computer-implemented method for designing a three-dimensional modeled physical part. The method comprises providing a boundary representation of the modeled physical part, selecting, upon user action, a subset of the boundary representation that represents a geometric feature, identifying all the subsets of the boundary representation that are similar to the selected subset of the boundary representation by applying a feature similarity function.

Systems and methods for determining and displaying reserve estimates for a reservoir by generating a table and, optionally, a report and/or a graph for the reserve estimates and predefined identification properties that uniquely describe the reserve estimates.