Intel Corporation

Aviad Zabatani

Erez Sperling

Michael Bronstein

Vitaly Surazhsky

Ron Kimmel

Alex Bronstein

Ohad Menashe

David Haim Silver

Sagy Bareket

Ofir Mulla

Alexander Bronstein

Sagi Ben Moshe

Ido Nissenboim

Omer Sella

Zachi Karni

Ohad Manashe

An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.

Online compensation of thermal distortions in a stereo depth camera

Example range estimation apparatus disclosed herein include a first signal processor to estimate a signal power parameter and a noise power parameter of a LIDAR system based on first data to be output from a light capturing device of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data to be output from the light capturing device, the second data associated with a modulated light beam projected by the LIDAR system, the templates generated based on the signal power parameter and the noise power parameter, and the second data to have a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor is also to determine, based on the templates, an estimated propagation delay associated with the second data.

Range estimation for light detecting and ranging (LIDAR) systems

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

Example range estimation apparatus disclosed herein include a first signal processor to process first data output from a light capturing device of a LIDAR system to estimate signal and noise power parameters of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data output from the light capturing device while a modulated light beam is projected by the LIDAR system, the templates generated based on the signal and noise power parameters, and the second data having a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor also cross-correlates the templates with the second data to determine an estimated propagation delay associated with the second data, the estimated propagation delay convertible to an estimated range to an object that reflected the modulated light beam.

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

In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera. For instance, a depth camera is described having therein a projector to project a collection of planes, each at a different angle of projection, onto a scene via a plurality of coded pattern images, each of the coded pattern images having encoded therein via a plurality of stripes, the angle of projection for the plane of projection within which the respective coded pattern image is projected; a detector to capture the plurality of coded pattern images from the scene; a processing component to adjust for ambient illumination and reflection properties of the scene; in which the processing component to further output a bit value for each pixel in the captured plurality of coded pattern images and to output a sub-pixel offset for the pixels positioned upon transitions of the plurality of stripes in the captured plurality of coded pattern images; a decoder to decode each of the plurality of coded pattern images and to adjust the decoded plurality of coded image patterns based on the sub-pixel offsets to determine the angle of projection for the corresponding plane of projection; and a triangulator to determine a position of an object in the scene based on an intersection of the determined angle of projection for the corresponding plane of projection with a known ray emanating from the detector that detected the plurality of the coded pattern images from the scene. Other related embodiments are disclosed.

Systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera

In accordance with disclosed embodiments, there is described a depth camera calibration system which includes: a depth camera to be calibrated; a calibration application to execute upon a mobile device, the calibration application to: (i) determine a precise image size of a calibration image to be displayed to a screen of the mobile device based on a screen size of the mobile device, the calibration image having embedded therein a plurality of objects of a known size, (ii) encode the known size of the objects into an optical machine-readable data representation, and (iii) display the encoded optical machine-readable data representation to the mobile device; in which the depth camera is to read the optical machine-readable data representation displayed by the mobile device to determine the known size of the objects of the calibration image; in which the calibration application is to display the calibration image to the mobile device; and in which an imager of the depth camera is to capture the objects of the coded image pattern and calibrate based on the known size of the objects of the captured calibration image. Other related embodiments are disclosed.

Dynamic depth camera system calibration using mobile dispay device

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 method and apparatus for performing temperature compensation for thermal distortions in a camera system. In one embodiment, the system comprises a first camera configured to capture a sequence of images of the object; a second device; a processing unit to receive the sequence of images and determine depth information in response to parameters of the camera and the second device; one or more temperature sensors; and a thermal correction unit responsive to temperature information from the one or more temperature sensors to adjust one or more of the calibration parameters of the first camera and the second device.

A method and apparatus for performing a single view depth and texture calibration are described. In one embodiment, the apparatus comprises a calibration unit operable to perform a single view calibration process using a captured single view a target having a plurality of plane geometries having detectable features and being at a single orientation and to generate calibration parameters to calibrate one or more of the projector and multiple cameras using the single view of the target.

Single-view feature-less depth and texture calibration

In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera. For instance, a depth camera is described having therein a projector to project a collection of planes, each at a different angle of projection, onto a scene via a plurality of coded pattern images, each of the coded pattern images having encoded therein via a plurality of stripes, the angle of projection for the plane of projection within which the respective coded pattern image is projected; a detector to capture the plurality of coded pattern images from the scene; a processing component to output a bit value for each pixel in the captured plurality of coded pattern images based on the pixel captured and based further on a patch of neighboring pixels surrounding the pixel; a decoder to decode each of the plurality of coded pattern images based on the bit values output by the processing component to determine the angle of projection for the corresponding plane of projection; and a triangulator to determine a position of an object in the scene based on an intersection of the determined angle of projection for the corresponding plane of projection with a geometric ray originating from the detector that detected the plurality of the coded pattern images. Other related embodiments are disclosed.

Growing community of inventors

Even Yehuda, Israel

Aviad Zabatani