An information processing apparatus extracts, from each of images of an object captured from a plurality of viewpoints, a contour of the object, selects at least one point included in the contour for each of the extracted contour, and associates the selected points between the images, derives three-dimensional coordinates of the associated points, derives a plane for approximating the object based on the derived three-dimensional coordinates of the points, and estimates depth values of unassociated points out of points on the contour based on the three-dimensional coordinates of the related points and the derived plane, to accurately estimate depth value of the object.

An information processing apparatus extracts, from each of images of an object captured from a plurality of viewpoints, a contour of the object, selects at least one point included in the contour for each of the extracted contour, and associates the selected points between the images, derives three-dimensional coordinates of the associated points, derives a plane for approximating the object based on the derived three-dimensional coordinates of the points, and estimates depth values of unassociated points out of points on the contour based on the three-dimensional coordinates of the related points and the derived plane, to accurately estimate depth value of the object.

A head mounted display (HMD) comprises an eye tracking system configured to perform a calibration process using an eye tracking system of the HMD that includes determining a pupillary axis and/or determining an angular offset between the pupillary axis and the eye's true line of sight. The eye tracking system obtains an eye model captures images of the user's pupil while the user is looking at a target or other content displayed on the HMD. In some embodiments, the calibration process is based on a single image of the user's eye and is performed only once. For example, the process can be performed the first time the user uses the HMD, which stores the calibration data for the user in a memory for future use.

A head mounted display (HMD) comprises an eye tracking system configured to perform a calibration process using an eye tracking system of the HMD that includes determining a pupillary axis and/or determining an angular offset between the pupillary axis and the eye's true line of sight. The eye tracking system obtains an eye model captures images of the user's pupil while the user is looking at a target or other content displayed on the HMD. In some embodiments, the calibration process is based on a single image of the user's eye and is performed only once. For example, the process can be performed the first time the user uses the HMD, which stores the calibration data for the user in a memory for future use.

Systems and methods are provided for receiving a two-dimensional (2D) image comprising a 2D object; identifying a contour of the 2D object; generating a three-dimensional (3D) mesh based on the contour of the 2D object; and applying a texture of the 2D object to the 3D mesh to output a 3D object representing the 2D object.

A motion analysis system and motion tracking system for capturing moved or moving objects that are thermally distinct from their surroundings. The system has a camera group with at least one thermographic camera, a calibration unit, a synchronization unit, a segmentation unit, a reconstruction unit, a projection unit and an identification unit. Such a system advantageously allows a thermally aided segmentation both of the 2D thermogragraphic images and of any 2D video images, specifically irrespective of the ambient conditions of an object to be analyzed and/or to be tracked and without requiring marker elements attached to the object.

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program and method for rendering three-dimensional captions (3D) in real-world environments depicted in image content. An editing interface is displayed on a client device. The editing interface includes an input component displayed with a view of a camera feed. A first input comprising one or more text characters is received. In response to receiving the first input, a two-dimensional (2D) representation of the one or more text characters is displayed. In response to detecting a second input, a preview interface is displayed. Within the preview interface, a 3D caption based on the one or more text characters is rendered at a position in a 3D space captured within the camera feed. A message is generated that includes the 3D caption rendered at the position in the 3D space captured within the camera feed.

The present invention provides a monocular unsupervised depth estimation method based on contextual attention mechanism, belonging to the technical field of image processing and computer vision. The invention adopts a depth estimation method based on a hybrid geometric enhancement loss function and a context attention mechanism, and adopts a depth estimation sub-network, an edge sub-network and a camera pose estimation sub-network based on convolutional neural network to obtain high-quality depth maps. The present invention uses convolutional neural network to obtain the corresponding high-quality depth map from the monocular image sequences in an end-to-end manner. The system is easy to construct, the program framework is easy to implement, and the algorithm runs fast; the method uses an unsupervised method to solve the depth information, avoiding the problem that ground-truth data is difficult to obtain in the supervised method.

The present invention provides a monocular unsupervised depth estimation method based on contextual attention mechanism, belonging to the technical field of image processing and computer vision. The invention adopts a depth estimation method based on a hybrid geometric enhancement loss function and a context attention mechanism, and adopts a depth estimation sub-network, an edge sub-network and a camera pose estimation sub-network based on convolutional neural network to obtain high-quality depth maps. The present invention uses convolutional neural network to obtain the corresponding high-quality depth map from the monocular image sequences in an end-to-end manner. The system is easy to construct, the program framework is easy to implement, and the algorithm runs fast; the method uses an unsupervised method to solve the depth information, avoiding the problem that ground-truth data is difficult to obtain in the supervised method.

A method generating a more accurate 3D model for at least two gemstones using external surface of the gemstones; storing, in memory, the more accurate 3D model of the first gemstone and the second gemstone; comparing the more accurate 3D model of the first gemstone and the more accurate 3D model of the second gemstone from the stored memory; calculating, based on the comparison, a matching score for the more accurate 3D model of the first gemstone and the more accurate 3D model of the second gemstone, the matching score being informative of a match between the first gemstone and the second gemstone; and identifying the first gemstone and the second gemstone as being the same gemstone when the matching score meets a predefined matching criterion.