Intel Corporation

David Haim Silver

Michael Bronstein

Alex Bronstein

Erez Sperling

Aviad Zabatani

Vitaly Surazhsky

Ron Kimmel

Ohad Menashe

Ofir Mulla

Ohad Manashe

A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Auto range control for active illumination depth camera

Arrangements (e.g., apparatus, system, method, article of manufacture) for reconstructing a depth image of a scene. Some embodiments include: a processor; and a non-transitory computer readable medium to store a set of instructions for execution by the processor, the set of instructions to cause the processor to perform various operations. Operations include: collecting multiple data sets for a code-modulated light pulse reflected from an object in a scene, with each data set associated with a direction in a set of directions for the reflected code-modulated light pulse; assigning a fitness value to each data set based on one or more parameters of a model; and reconstructing a depth image providing a depth at each direction based on a corresponding data set and fitness value, the depth to correspond with a round-trip delay time of the code-modulated light pulse.

Range reconstruction using shape prior

An example apparatus for calibrating texture cameras includes an image receiver to receive a depth image from a depth camera and a color image from a texture camera. The apparatus also includes a feature extractor to extract features from the depth image and the color image. The apparatus further includes a feature tester to detect that the extracted features from the depth image and the color image exceed a quality threshold. The apparatus includes a misalignment detector to detect a misalignment between the extracted features from depth image and the extracted features from color image exceeds a misalignment threshold. The apparatus also further includes a calibrator to modify calibration parameters for the texture camera to reduce the detected misalignment between the extracted features from the depth image and the extracted features from the color image below a misalignment threshold.

Calibrating texture cameras using features extracted from depth images

A mechanism is described for facilitating three-dimensional (3D) depth imaging systems, and morphological and geometric filters for edge enhancement in depth images at computing devices according to one embodiment. A method of embodiments, as described herein, includes detecting an input digital image of an object, the digital image comprising data pixels contaminated by noise and confidence values corresponding to the data pixels, and computing a morphological filter by matching the confidence pixels in the input digital image with a set of matching templates, and using a set of masking templates to determine the data pixels and confidence pixels in the filtered image. The method further include computing an edge filter by performing computation of distances between the data pixels along a plurality of directions to determine an edge direction, and determining the data pixels and and the confidence pixels in a filtered image based on the edge direction. The method may further include applying at least one of the morphological filter and the edge filter to filter the digital image.

Morphological and geometric edge filters for edge enhancement in depth images

A mechanism is described for facilitating code filters for coded light depth acquisition in depth images at computing devices according to one embodiment. A method of embodiments, as described herein, includes detecting a code image of an object comprising pixels of code values and pixels of metadata values including confidence and code transition locations, and computing a vertical filter to be applied to the code image to smooth out the code transitions along vertical directions. The method further include computing a horizontal filter to be applied to the code image to smooth out the code transitions along horizontal directions, and computing a consistency filter to be applied to the code image to increase an accuracy of the code values and mark inconsistent pixels as invalid. The method may further include applying at least one of one or more of the vertical filter, the horizontal filter, and the consistency filter to filter the digital image, and outputting the filtered code image comprising filtered code values and filtered metadata values.

Growing community of inventors

Haifa, Israel

David Haim Silver