The invention relates to a method and a system for measuring an object (2) by means of stereoscopy, in which method a pattern (3) is projected onto the object surface by means of a projector (9) and the pattern (3), which is designated as a scene and is projected onto the object surface, is captured by at least two cameras (4.1, 4.2, 4.3, 4.4), wherein correspondences of the scene are found in the images captured by the cameras (4.1, 4.2, 4.3, 4.4) by means of a computing unit (5) using image processing, and the object (2) is measured by means of the correspondences found. According to the invention, the cameras (4.1, 4.2, 4.3, 4.4) are intrinsically and extrinsically calibrated, and a two-dimensional and temporal coding is generated during the pattern projection, by (a) projecting a (completely) two-dimensionally coded pattern (3) and capturing the scene using the cameras (4.1, 4.2, 4.3, 4.4), and (b) projecting a temporally encoded pattern having a two-dimensionally different coding several times in succession and using the cameras (4.1, 4.2, 4.3, 4.4) to capture several scenes in succession, the capturing of said scenes being triggered simultaneously in each case.

An imaging apparatus includes an image sensor configured to photoelectrically convert an object image formed by an imaging optical system in at least three states in which positions of light sources configured to emit light are different from each other, and to output at least three image data, and a luminance distribution acquirer configured to acquire information on a plurality of luminance distributions of the image data based on common information on a common light amount distribution regarding the at least three states.

A system for disparity estimation includes one or more feature extractor modules configured to extract one or more feature maps from one or more input images; and one or more semantic information modules connected at one or more outputs of the one or more feature extractor modules, wherein the one or more semantic information modules are configured to generate one or more foreground semantic information to be provided to the one or more feature extractor modules for disparity estimation at a next training epoch.

An immersive content presentation system can capture the motion or position of a performer in a real-world environment. A game engine can be modified to receive the position or motion of the performer and identify predetermined gestures or positions that can be used to trigger actions in a 3-D virtual environment, such as generating a digital effect, transitioning virtual assets through an animation graph, adding new objects, and so forth. The use of the 3-D environment can be rendered and composited views can be generated. Information for constructing the composited views can be streamed to numerous display devices in many different physical locations using a customized communication protocol. Multiple real-world performers can interact with virtual objects through the game engine in a shared mixed-reality experience.

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.

A depth acquisition device includes a memory and a processor performing: acquiring, from the memory, intensities of infrared light measured by imaging with infrared light emitted from a light source and reflected on a subject by pixels in an imaging element; generating a depth image by calculating the distance for each pixel based on the intensities; acquiring, from the memory, a visible light image generated by imaging, with visible light, the substantially same scene from the substantially same viewpoint at the substantially same timing as those of the infrared light image; detecting a lower reflection region showing an object having a lower reflectivity from the infrared light image in accordance with the infrared light image and the visible light image; correcting a corresponding lower reflection region in the depth image in accordance with the visible light image; and outputting the depth image with the corrected lower reflection region.

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.

The embodiments describes herein relate generally to capturing a plurality of frames (i.e., image frames) of an object and utilizing those plurality of frames to render a 3D image of the object. The process to render a 3D image consists at least of two phases, a capturing phase and a reconstruction phase. During the capture phase a plurality of frames may be captured of an object and based upon these plurality of frames a 3D model of an object may be rendered by a computational inexpensive algorithm. By utilizing a computational inexpensive algorithm mobile devices may be able to successfully render 3D models of objects.

Systems and methods in accordance with embodiments of the invention estimate depth from projected texture using camera arrays. One embodiment of the invention includes: at least one two-dimensional array of cameras comprising a plurality of cameras; an illumination system configured to illuminate a scene with a projected texture; a processor; and memory containing an image processing pipeline application and an illumination system controller application. In addition, the illumination system controller application directs the processor to control the illumination system to illuminate a scene with a projected texture. Furthermore, the image processing pipeline application directs the processor to: utilize the illumination system controller application to control the illumination system to illuminate a scene with a projected texture capture a set of images of the scene illuminated with the projected texture; determining depth estimates for pixel locations in an image from a reference viewpoint using at least a subset of the set of images. Also, generating a depth estimate for a given pixel location in the image from the reference viewpoint includes: identifying pixels in the at least a subset of the set of images that correspond to the given pixel location in the image from the reference viewpoint based upon expected disparity at a plurality of depths along a plurality of epipolar lines aligned at different angles; comparing the similarity of the corresponding pixels identified at each of the plurality of depths; and selecting the depth from the plurality of depths at which the identified corresponding pixels have the highest degree of similarity as a depth estimate for the given pixel location in the image from the reference viewpoint.

Techniques are provided for recognition of activity in a sequence of video image frames that include depth information. A methodology embodying the techniques includes segmenting each of the received image frames into a multiple windows and generating spatio-temporal image cells from groupings of windows from a selected sub-sequence of the frames. The method also includes calculating a four dimensional (4D) optical flow vector for each of the pixels of each of the image cells and calculating a three dimensional (3D) angular representation from each of the optical flow vectors. The method further includes generating a classification feature for each of the image cells based on a histogram of the 3D angular representations of the pixels in that image cell. The classification features are then provided to a recognition classifier configured to recognize the type of activity depicted in the video sequence, based on the generated classification features.