The present invention relates to a remote point method. A remote pointing method according to the present invention comprises capturing images by a first and a second camera disposed being separated spatially from each other; detecting a pointing part in a first image captured by the first camera; determining a region of interest including the pointing part in a second image captured by the second camera; and extracting stereoscopic coordinates of the pointing part within the region of interest.

A method for automatic registration of 3D image data, captured by a 3D image capture system having an RGB camera and a depth camera, includes capturing 2D image data with the RGB camera at a first pose; capturing depth data with the depth camera at the first pose; performing an initial registration of the RGB camera to the depth camera; capturing 2D image data with the RGB camera at a second pose; capturing depth data at the second pose; and calculating an updated registration of the RGB camera to the depth camera.

A method for displaying virtual content to a user, the method includes determining an accommodation of the user's eyes. The method also includes delivering, through a first waveguide of a stack of waveguides, light rays having a first wavefront curvature based at least in part on the determined accommodation, wherein the first wavefront curvature corresponds to a focal distance of the determined accommodation. The method further includes delivering, through a second waveguide of the stack of waveguides, light rays having a second wavefront curvature, the second wavefront curvature associated with a predetermined margin of the focal distance of the determined accommodation.

A hand-raising detection device includes a converter and a detection unit. The converter performs conversion of a predetermined space including a person into an overhead view image by using a result of three dimensional measurement performed on the predetermined space. The detection unit performs detection of a hand-raising action by using a silhouette image of the person in the overhead view image resulting from the conversion performed by the converter.

Multiple digital cameras view a large scene, such as a part of a city. Some of the cameras view different parts of that scene, and video feeds from the cameras are processed at a computer to generate a photo-realistic synthetic 3D model of the scene. This enables the scene to be viewed from any viewing angle, including angles that the original, real cameras do not occupy—i.e. as though viewed from a ‘virtual camera’ that can be positioned in any arbitrary position. The 3D model combines both static elements that do not alter in real-time, and also dynamic elements that do alter in real-time or near real-time.

An apparatus and computer-implemented method for generating a three-dimensional scene and non-transitory tangible computer readable medium thereof are provided. The apparatus comprises a storage unit and a processing unit. The storage unit is stored with an image and a plurality of depth data of the image. The processing unit is configured to generate a depth histogram according to the depth data, generate a region of interest of the image according to the depth histogram, decide a moving path of a virtual camera according to the region of interest, and generate the three-dimensional scene according to the image and the moving path.

The present invention discloses a multispectral stereo camera self-calibration algorithm based on track feature registration, and belongs to the field of image processing and computer vision. Optimal matching points are obtained by extracting and matching motion tracks of objects, and external parameters are corrected accordingly. Compared with an ordinary method, the present invention uses the tracks of moving objects as the features required for self-calibration. The advantage of using the tracks is good cross-modal robustness. In addition, direct matching of the tracks also saves the steps of extraction and matching the feature points, thereby achieving the advantages of simple operation and accurate results.

An imaging device assembly includes a light source, an imaging device formed with a plurality of imaging elements, and a control device. Each imaging element (10) includes a light receiving portion (21), a first charge storage portion (22) and a second charge storage portion (24), and a first charge transfer control means (23) and a second charge transfer control means (24). Under the control of the control device, the imaging element (10) captures an image of an object on the basis of high-intensity light and stores first image signal charge into the first charge storage portion (22) during a first period, and captures an image of the object on the basis of low-intensity light and stores second image signal charge into the second charge storage portion (24) during a second period. The control device obtains an image signal on the basis of the difference between the first image signal charge and the second image signal charge.

A camera tracking system for computer assisted navigation during surgery operatively determines a first pose of a second extended-reality (XR) headset relative to stereo tracking cameras located on a first XR headset based on first tracking information from the stereo tracking cameras. The camera tracking system determines a second pose of eyes of a user wearing the second XR headset relative to the stereo tracking cameras located on the first XR headset based on second tracking information from the stereo tracking cameras. The camera tracking system also calibrates an eye-to-display relationship defining pose of the eyes of the user wearing the second XR headset to a display device of the second XR headset based on the determined first and second poses. The camera tracking system also controls where symbols are displayed on the display device of the second XR headset based on the eye-to-display relationship.

A processing device generates composite images from a sequence of images. The composite images may be used as frames of video. A foreground/background segmentation is performed at selected frames to extract a plurality of foreground object images depicting a foreground object at different locations as it moves across a scene. The foreground object images are stored to a foreground object list. The foreground object images in the foreground object list are overlaid onto subsequent video frames that follow the respective frames from which they were extracted, thereby generating a composite video.