Systems and methods relate to receiving a plurality of video streams captured of a subject by a plurality of video cameras, each video stream including video frames time-synchronized according to a shared frame rate, each video camera having a known vantage point in a predetermined coordinate system; obtaining at least one three-dimensional (3D) mesh of the subject at the shared frame rate, the 3D mesh time-synchronized with the video frames of the video streams, the at least one mesh including a plurality of vertices with known locations in the predetermined coordinate system; calculating one or more lists of visible-vertices at the shared frame rate, each list including a subset of the plurality of vertices of the at least one 3D mesh of the subject, the subset being a function of the location of the known vantage point associated with at least one of the plurality of video cameras; generating one or more time-synchronized data streams at the shared frame rate, the one or more time-synchronized data streams including: one or more video streams encoding at least one of the plurality of video streams; and one or more geometric-data streams including the calculated one or more visible-vertices lists; and transmitting the one or more time-synchronized data streams to a receiver for rendering of a viewpoint-adaptive 3D persona of the subject.

According to one embodiment, an image processing apparatus includes a memory and one or more hardware processors electrically coupled to the memory. The one or more hardware processors acquire a first image of an object including a first shaped blur and a second image of the object including a second shaped blur. The first image and the second image are acquired by capturing at a time through a single image-forming optical system. The one or more hardware processors acquire distance information to the object based on the first image and the second image, with a statistical model that has learnt previously.

A method includes obtaining at least three input image frames of a scene captured using at least three imaging sensors. The input image frames include a reference image frame and multiple non-reference image frames. The method also includes generating multiple disparity maps using the input image frames. Each disparity map is associated with the reference image frame and a different non-reference image frame. The method further includes generating multiple confidence maps using the input image frames. Each confidence map identifies weights associated with one of the disparity maps. In addition, the method includes generating a depth map of the scene using the disparity maps and the confidence maps. The imaging sensors are arranged to define multiple baseline directions, where each baseline direction extends between the imaging sensor used to capture the reference image frame and the imaging sensor used to capture a different non-reference image frame.

Systems and methods for computing a transformation for correction motion between a first medical image and a second medical image are provided. One or more landmarks are detected in the first medical image and the second medical image. A first tree of the anatomical structure is generated from the first medical image based on the one or more landmarks detected in the first medical image and a second tree of the anatomical structure is generated from the second medical image based on the one or more landmarks detected in the second medical image. The one or more landmarks detected in the first medical image are mapped to the one or more landmarks detected in the second medical image based on the first tree and the second tree. A transformation to align the first medical image and the second medical image is computed based on the mapping.

Disclosed herein are methods, systems, and computer-readable storage media for generating three-dimensional (3D) images of a scene. According to an aspect, a method includes capturing a real-time image and a first still image of a scene. Further, the method includes displaying the real-time image of the scene on a display. The method also includes determining one or more properties of the captured images. The method also includes calculating an offset in a real-time display of the scene to indicate a target camera positional offset with respect to the first still image. Further, the method includes determining that a capture device is in a position of the target camera positional offset. The method also includes capturing a second still image. Further, the method includes correcting the captured first and second still images. The method also includes generating the three-dimensional image based on the corrected first and second still images.

An image device includes a first image capture module, a second image capture module, and an image processor. The first image capture module has a first field of view, and the second image capture module has a second field of view different from the first field of view. The image processor is coupled to the first image capture module and the second image capture module. The image processor sets a virtual optical center according to the first image capture module, the second image capture module, and a target visual scope, and generates a display image corresponding to the virtual optical center.

The present teaching relates to method, system, medium, and implementation of determining depth information in autonomous driving. Stereo images are first obtained from multiple stereo pairs selected from at least two stereo pairs. The at least two stereo pairs have stereo cameras installed with the same baseline and in the same vertical plane. Left images from the multiple stereo pairs are fused to generate a fused left image and right images from the multiple stereo pairs are fused to generate a fused right image. Disparity is then estimated based on the fused left and right images and depth information can be computed based on the stereo images and the disparity.

An information processing device includes a controller. In a case where multiple images are formed in air in a depth direction, the controller controls a display of at least one of the images corresponding to one position or multiple positions in accordance with a command from a user.

Multiple snapshots of a scene are captured within an executing application (e.g., a video game). When each snapshot is captured, associated color values per pixel and a distance or depth value z per pixel are stored. The depth information from the snapshots is accessed, and a point cloud representing the depth information is constructed. A mesh structure is constructed from the point cloud. The light field(s) on the surface(s) of the mesh structure are calculated. A surface light field is represented as a texture. A renderer uses the surface light field with geometry information to reproduce the scene captured in the snapshots. The reproduced scene can be manipulated and viewed from different perspectives.

A method includes defining a disparity range having discrete disparities and taking first, second, and third images of a spatial region using first, second, and third imaging units. The imaging units are arranged in an isosceles triangle geometry. The method includes determining first similarity values for a pixel of the first image for all the discrete disparities along a first epipolar line associated with the pixel in the second image. The method includes determining second similarity values for the pixel for all discrete disparities along a second epipolar line associated with the pixel in the third image. The method includes combining the first and second similarity values and determining a common disparity based on the combined similarity values. The method includes determining a distance to a point within the spatial region for the pixel from the common disparity and the isosceles triangle geometry.