An electronic device is disclosed. The present electronic device comprises a display, a processor electrically connected with the display so as to control the display, and a memory electrically connected with the processor, wherein the memory includes at least one command, and the processor, by executing at least one command, acquires a plurality of object images corresponding to a plurality of objects in a two-dimensional image, acquires a plurality of three-dimensional modeling images corresponding to the plurality of object images and information related to the plurality of objects by applying the plurality of object images to a plurality of network models, and displays, on the display, the three-dimensional images including the plurality of three-dimensional modeling images on the basis of the information related to the plurality of objects and the location information about the plurality of objects in the two-dimensional image.

The present application discloses a verification method and device for a modeling route, an unmanned vehicle, and a storage medium, which relate to the technical field of computer vision and intelligent transportation. A specific implementation of the method in the present application lies in: acquiring a filtering threshold of a target road section, where the filtering threshold is related to image feature points corresponding to the target road section; verifying a modeling route corresponding to the target road section through the filtering threshold to obtain a verification result. According to the present application, availability of the modeling route can be directly verified with the filtering threshold while there is no need to verify the modeling route through manual driving of the vehicle, thereby effectively increasing the verification efficiency, protecting the vehicle from travelling along an unavailable modeling route and improving the driving experience.

An illustrative augmented reality tracking system obtains a volumetric feature descriptor dataset that includes: 1) a plurality of feature descriptors associated with a plurality of views of a volumetric target, and 2) a plurality of 3D structure datapoints that correspond to the plurality of feature descriptors. The system also obtains an image frame captured by a user equipment (UE) device. The system identifies a set of image features depicted in the image frame and detects, based on a match between the set of image features depicted in the image frame and a set of feature descriptors of the plurality of feature descriptors, that the volumetric target is depicted in the image frame. In response to this detecting and based on 3D structure datapoints corresponding to matched feature descriptors, the system determines a spatial relationship between the UE device and the volumetric target. Corresponding methods and systems are also disclosed.

The present disclosure provides method for generating a three-dimensional (3D) model of a dental structure of a subject. The method comprises: capturing image data about the dental structure of the subject using a camera of a mobile device; constructing a first 3D model of the dental structure from the image data; registering the first 3D model with an initial 3D surface model to determine a transformation for at least one element of the dental structure; and updating the initial 3D surface model by (i) applying the transformation to update a position of the at least one element and/or (ii) deforming a surface of a local area of the at least one element using a deformation algorithm.

Methods and systems described herein are directed to creating an artificial reality environment having elements automatically created from source images. In response to a creation system receiving the source images, the system can employ a multi-layered comparative analysis to obtain virtual object representations of objects depicted in the source images. A first set of the virtual objects can be selected from a library by matching identifiers for the depicted objects with tags on virtual objects in the library. A second set of virtual objects can be objects for which no candidate first virtual objects was adequately matched in the library, prompting the creation of a virtual object by generating depth data and skinning a resulting 3D mesh based on the source images. Having determined the virtual objects, the system can compile them into the artificial reality environment according to relative locations determined from the source images.

An artificial neural network-based method for selecting a surface type of an object includes receiving at least one object image, performing surface type identification on each of the at least one object image by using a first predictive model to categorize the object image to one of a first normal group and a first abnormal group, and performing surface type identification on each output image in the first normal group by using a second predictive model to categorize the output image to one of a second normal group and a second abnormal group.

A device determines the positioning of objects in a scene by implementing a robust and deterministic method. The device obtains object detection data (ODD) which identifies the objects and locations of reference points of the objects in views of the scene. The obtained ODD is processed to identify a first image object of a first view as a mirror reflection of a real object. A virtual view associated with a virtual camera position is created, including the ODD associated with the first image object of the first view. The ODD associated with the first image object is removed from the first view. Based on the ODD associated with at least said virtual view and a further view of the one or more views, a position of said first image object is computed.

Systems and methods for generating a three-dimensional reconstruction from a two-dimensional image of an asset are described. In some aspects, at least one computer hardware processor is used to perform: receiving a two-dimensional input image of an asset; encoding, using a first statistical model, the input image into a latent vector; and generating, using a second statistical model, at least one depth image from the latent vector, wherein pixel values in the at least one depth image correspond to coordinates of a point cloud.

A system is provided which utilizes multiple combinations of object location technology to locate objects and direct users to them, and which provides reliable owner recognition and ownership verification with the use of displayed augmented reality with a predefined image of the object and/or the user. Further, the system utilizes augmented reality fingerprint markers. When the augmented reality fingerprint marker is positioned on an object and scanned with a smart device, information relating to the object is superimposed on the object displayed on the smart device.

A three-dimensional data augmentation method includes that an original two-dimensional image and truth value annotation data matching the original two-dimensional image are acquired, that the original two-dimensional image and two-dimensional truth value annotation data are transformed according to a target transformation element to obtain a transformed two-dimensional image and transformed two-dimensional truth value annotation data, that an original intrinsic matrix is transformed according to the target transformation element to obtain a transformed intrinsic matrix, that a two-dimensional projection is performed on three-dimensional truth value annotation data according to the transformed intrinsic matrix to obtain projected truth value annotation data, and that three-dimensional augmentation image data is generated according to the transformed two-dimensional image, the transformed two-dimensional truth value annotation data, and the projected truth value annotation data.