A method for automated analysis of data obtained from biologic, or non-biologic, material is provided. The method includes extracting, in a visualization of the material, first shapes that combine to form a target shape. The method also includes registering the first shape of the target shape to second shapes of a generic shape, and identifying variations between the first shapes and the second shapes. A system for analyzing biologic material is provided that includes an extraction engine a registration engine an identification engine a display an atlas adapted to provide the generic shape and a database for storing the visualization of the target shape. A non-transitory computer-readable medium storing a program for analyzing biologic material is provided. The program includes instructions that, when executed by a processor, causes a processor to execute the method.

Systems, methods, and computer-readable media for providing a localization processing service for enabling localization of a navigation network-restricted subsystem are provided.

An image processing method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: displaying, in a user interface, a picture obtained by observing a virtual environment from a virtual camera, the picture including a distant view portion corresponding to a virtual area of the virtual environment, the virtual area being outside a preset range associated with the virtual camera; detecting an editing operation performed on a virtual model in the virtual area; and updating, in response to determining the edited virtual model meets a specific condition, the distant view portion based on the edited virtual model.

A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

The present invention relates to a method for generating three-dimensional image from two-dimensional images, and more specifically, a method for generating three-dimensional image of human bones from two 2D planar images thereof. The method comprises the steps of: providing a first X-ray planar image and a second X-ray planar image; predicting one set of predicted posture parameters for each of the first X-ray planar image and the second X-ray planar image; and generating the data of a stereoscopic image according to the first X-ray planar image, the second X-ray planar image, and the predicted posture parameters. The present invention also relates to a method for training an artificial intelligence to perform three-dimensional image generation described above.

Disclosed herein are methods and systems for building and updating a virtual store based on a physical store. A computing system is structured to receive captured images of a physical space having a set of products. The physical space is simulated by a three-dimensional (3D) model of the physical space. A corresponding representation of the set of products is positioned relative to the 3D model. The computing system is structured to detect, based on the captured images, product information for products in the physical space and generate, based on the product information, an updated representation of the set of products based on the product information. The updated representation of the set of products corresponds to the products in the physical space as shown in the captured images.

An object is to appropriately determine a color of a three-dimensional model of an object. In an image processing apparatus, based on the analysis of the three-dimensional model of the object, the determination method of a color for the portion, which corresponds to the object shape of the component constituting the three-dimensional model, is made different from that for the portion, which does not correspond to the object shape.

A method for 3D reconstruction includes: acquiring an image sequence of an object to be reconstructed continuously acquired by a monocular image collector; extracting depth information of an image to be processed in the image sequence; estimating translation attitude information of the image to be processed based on world coordinate information of each feature point in a reference image, image coordinate information of each feature point in the image to be processed, and rotation attitude information of the image to be processed, the reference image being an adjacent image whose acquisition time point in the image sequence is located before the image to be processed; generating a point cloud image based on the depth information, the rotation attitude information and the translation attitude information of each image; and performing 3D reconstruction on the object to be reconstructed based on the point cloud image.

A method and to a device for processing a 3D point cloud representing surroundings, which is generated by a sensor. Initially, starting cells are identified based on ascertained starting ground points within the 3D point cloud which meet at least one predefined ground point criterion with respect to a reference plane divided into cells. Thereafter, cell planes are ascertained for the respective starting cells of the reference plane. Thereafter, estimated cell planes and ground points are ascertained for candidate cells deviating from the starting cells based on the cell planes of the starting cells, which are subsequently converted into final cell planes. As a result of such a cell growth originating from the starting cells, the cells of the reference plane are iteratively run through and processed so that the 3D point cloud is reliably classifiable into ground points and object points based on this method.