Analog Devices,inc.

Analog Devices International Unlimited Company

Charles Mathy

Nicolas Le Dortz

Sefa Demirtas

Tao Yu

Brian C Donnelly

Jonathan Ephraim David Hurwitz

Eoin Edward English

Javier Calpe Maravilla

Erik D Barnes

Bin Huo

Atulya Yellepeddi

Krystian Balicki

Jonathan Ari Goldberg

Neal Tait Kurfiss

Foroohar Foroozan

Richard Haltmaier

Di Xue

Sunrita Poddar

Miles Bennett

Dhruvesh Gajaria

Edward Joseph Danyliw

James Noraky

Kevin Joseph Mcandrew

A depth estimation system can use a guided filter to enhance depth estimation using brightness image. Light is projected onto an object. The object reflects at least a portion of the projected light. The reflected light is at least partially captured by an image sensor. The depth estimation system may generate a depth image based on a phase shift between the captured light and the projected light and generate a brightness image based on brightness of the captured light. The depth estimation system may use the guided filter to identify a pixel that represents at least a portion of a boundary of the object. The guided filter can determine a depth value of the pixel based on the value of the corresponding pixel in the brightness image. The depth estimation system can assign the depth value to the pixel and generates an enhanced depth image.

Using guided filter to enhance depth estimation with brightness image

Sensor device

Time of Flight (ToF) depth image processing methods are disclosed for resolving corruption of ToF depth images. In ToF depth imaging, the light can travel paths of different lengths before returning to the pixel. Thus, the light hitting the pixel can have travelled different distances, and the distance obtained from an estimation procedure assuming a single distance may be spurious. Systems and methods are disclosed for including a time-of-flight imager and processor which resolves the multiple paths, and outputs the multiple depths at each pixel.

Resolving multi-path corruption of time-of-flight depth images

An image processing system having on-the-fly calibration uses the placement of the imaging sensor and the light source for calibration. The placement of the imaging sensor and light source with respect to each other affect the amount of signal received by a pixel as a function of distance to a selected object. For example, an obstruction can block the light emitter, and as the obstruction is positioned an increasing distance away from the light emitter, the signal level increases as light rays leave the light emitters, bounce off the obstruction and are received by the imaging sensor. The system includes a light source configured to emit light, and an image sensor to collect incoming signals including reflected light, and a processor to determine a distance measurement at each of the pixels and calibrate the system.

Built-in calibration of time-of-flight depth imaging systems

Time of Flight (ToF) depth image processing methods. Depth edge preserving filters are disclosed with superior performance to standard edge preserving filters applied to depth maps. In particular, depth variance is estimated and used to filter while preserving depth edges. In doing so, filter strength is calculated which can be used as an edge detector. A confidence map is generated with low confidence at pixels straddling a depth edge, and which reflects the reliability of the depth measurement at each pixel.

Time-of-flight depth image processing systems and methods

Depth imagers can implement time-of-flight operations to measure depth or distance of objects. A depth imager can emit light onto a scene and sense light reflected back from the objects in the scene using an array of sensors. Timing of the reflected light hitting the array of sensors gives information about the depth or distance of objects in the scene. In some cases, corrupting light that is outside of a field of view of a pixel in the array of sensors can hit the pixel due to internal scattering or internal reflections occurring in the depth imager. The corrupting light can corrupt the depth or distance measurement. To address this problem, an improved depth imager can isolate and measure the corrupting light due to internal scattering or internal reflections occurring in the depth imager, and systematically remove the measured corrupting light from the depth or distance measurement.

Measuring and removing the corruption of time-of-flight depth images due to internal scattering

Aspects of the embodiments are directed to passive depth determination. Initially, a high power depth map of a scene can be created. An object in the scene can be identified, such as a rigid body or other object or portion of an object. A series of lower power or RGB images can be captured. The object can be located in one or more of the lower power or RGB images. A change in the position of an object, represented by a set of pixels, can be determined. From the change in position of the object, a new depth of the object can be extrapolated. The extrapolated depth of the object can be used to update the high power depth map.

Reducing power consumption for time-of-flight depth imaging

Aspects of the embodiments are directed to methods and imaging systems. The imaging systems can be configured to sense, by an light sensor of the imaging system, light received during a time period, process the light received by the light sensor, identify an available measurement period for the imaging system within the time period based on the processed light, and transmit and receive light during a corresponding measurement period in one or more subsequent time periods.

Interference handling in time-of-flight depth sensing

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Arlington, MA, United States of America

Charles Mathy