Online compensation of thermal distortions in a stereo depth camera

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.

FIELD OF THE INVENTION

Embodiments of the present invention relate to the field of camera systems that perform three-dimensional (3-D) measurements by use of active or passive triangulation capturing one or more images with or without the assistance of one or more patterns projected by a projector; more particularly, embodiments of the present invention relate to performing temperature compensation of such camera systems.

BACKGROUND OF THE INVENTION

Stereo depth cameras are well-known and are often used to measure a distance from an object. As part of the process, images of scenes are captured and measurements are taken to determine depth information. One method of determining the depth of objects in a scene from a captured image is made using triangulation.

In order to perform triangulation accurately, some calibration of the stereo depth camera is typically performed at assembly time. Calibration sets some intrinsic and extrinsic parameters of the stereo camera. Subsequent calibrations may be needed due to the system's deviation from initial calibration due to various external factors, such as temperature, humidity or barometric pressure changes, or displacement of sensing and/or projecting devices included in the system.

Stereo depth cameras are very sensitive to tiny changes in the optic elements intrinsic (e.g., Field of View or focal length) and extrinsic parameters. As a result of these changes, key depth image characteristics are biased such as absolute depth value, world coordinate y-axis yaw, scaling effects and more. Temperature variations may cause changes in the parameters, thereby causing errors in the determined depth generated by the triangulation process. Current solutions to these problems caused by temperature either ignore this source of distortion, or apply active thermal control to maintain the temperature at which the system was initially calibrated. The former approach is very weak for a real product since it implies a narrow working temperature range and the system being at the “working point” where the temperature is stable. The active thermal control allows correct operation in a wider range of temperatures, yet it consumes power, area and cost, which are all undesirable.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

FIG. 1illustrates one embodiment of an active coded light triangulation system. The system includes coded light range cameras operating by projecting a sequence of one-dimensional binary (“black” and “white”) patterns onto a scene, such that the produced binary code encodes the angle of the projection plane. Depth is then reconstructed by triangulating the intersection of a ray emanating from the camera with the plane emanating from the projector.

Referring toFIG. 1, capture device100may include a 3D scanner, a 3D camera or any other device configured for a 3D object acquisition. In some embodiments, as illustrated, capture device100includes an image capturing device102(e.g., a digital camera) and a projector unit104, such as a laser projector or laser scanner, having a number of components. In some embodiments, digital camera102may comprise an infrared (IR) camera, and the projector unit104may comprise an IR projector.

Projector unit104is configured to project a light pattern as described above and may comprise a one-dimensional coded projector. In one embodiment, the light patterns comprise one-dimensional coded light patterns, e.g., the patterns that may be described by one-dimensional or linear codes. The light patterns formed by the laser planes on a surface of the object may be received by image capturing device102and sensed (e.g., read) by a sensor of image capturing device102. Based on the readings of the multiple scans of the light patterns accumulated during a sensing cycle of the sensor, capture device100may be configured to reconstruct the shape of the object.

In some embodiments, capture device100may further include another image capturing device, such as digital camera103. In some embodiments, digital camera103may have a resolution that is different than that of digital camera103. For example, digital camera102may be a multi-chromatic camera, such as red, green, and blue (RGB) camera configured to capture texture images of an object.

Capture device100may further include a processor106that may be in operative communication with the image camera component101over a bus or interconnect107. Processor106may include a standardized processor, a specialized processor, a microprocessor, or the like that may execute instructions that may include instructions for generating depth information, generating a depth image, determining whether a suitable target may be included in the depth image, or performing other operations described herein.

Processor106may be configured to reconstruct the object based on the images captured by digital camera102, for example, using geometry techniques or other techniques used for 3D image reconstruction. Processor106may be further configured to dynamically calibrate capture device100to correct distortions in the reconstructed image of the object that may be caused, for example, by various external factors (e.g., temperature).

Capture device100may further include a memory105that may store the instructions that may be executed by processor106, images or frames of images captured by the cameras, user profiles or any other suitable information, images, or the like. According to one example, memory105may include random access memory (RAM), read only memory (ROM), cache, Flash memory, a hard disk, or any other suitable storage component. As shown inFIG. 1, memory component105may be a separate component in communication with the cameras101and processor106. Alternatively, memory105may be integrated into processor106and/or the image capture cameras101. In one embodiment, some or all of the components102-106are located in a single housing.

Processor105, memory104, other components (not shown), image capturing device102, and projector unit104may be coupled with one or more interfaces (not shown) configured to facilitate information exchange among the above-mentioned components. Communications interface(s) (not shown) may provide an interface for device100to communicate over one or more wired or wireless network(s) and/or with any other suitable device. In various embodiments, capture device100may be included to or associated with, but is not limited to, a server, a workstation, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a handset, a tablet, a smartphone, a netbook, ultrabook, etc.).

In one embodiment, capture device100is integrated into a computer system (e.g., laptop, personal computer (PC), etc.). However, capture device100can be alternatively configured as a standalone device that is couplable to such a computer system using conventional technologies including both wired and wireless connections.

In various embodiments, capture device100may have more or less components, and/or different architectures. For example, in some embodiments, capture device100may include one or more of a camera, a keyboard, display such as a liquid crystal display (LCD) screen (including touch screen displays), a touch screen controller, non-volatile memory port, antenna or multiple antennas, graphics chip, ASIC, speaker(s), a battery, an audio codec, a video codec, a power amplifier, a global positioning system (GPS) device, a compass, an accelerometer, a gyroscope, and the like. In various embodiments, capture device100may have more or less components, and/or different architectures. In various embodiments, techniques and configurations described herein may be used in a variety of systems that benefit from the principles described herein.

Capture device100may be used for a variety of purposes, including, but not limited to, being part of a target recognition, analysis, and tracking system to recognize human and non-human targets in a capture area of the physical space without the use of special sensing devices attached to the subjects, uniquely identify them, and track them in three-dimensional space. Capture device100may be configured to capture video with depth information including a depth image that may include depth values via any suitable technique including, for example, triangulation, time-of-flight, structured light, stereo image, or the like.

Capture device100may be configured to operate as a depth camera that may capture a depth image of a scene. The depth image may include a two-dimensional (2D) pixel area of the captured scene where each pixel in the 2D pixel area may represent a depth value such as a distance in, for example, centimeters, millimeters, or the like of an object in the captured scene from the camera. In this example, capture device100includes an IR light projector404, an IR camera102, and a visible light RGB camera103that are configured in an array.

Various techniques may be utilized to capture depth video frames. For example, capture device100may use structured light to capture depth information. In such an analysis, patterned light (i.e., light displayed as a known pattern such as a grid pattern or a stripe pattern) may be projected onto the capture area via, for example, IR light projector104. Upon striking the surface of one or more targets or objects in the capture area, the pattern may become deformed in response. Such a deformation of the pattern may be captured by, for example, the IR camera102and/or the RGB camera103and may then be analyzed to determine a physical distance from capture device100to a particular location on the targets or objects.

Capture device100may utilize two or more physically separated cameras that may view a capture area from different angles, to obtain visual stereo data that may be resolved to generate depth information. Other types of depth image arrangements using single or multiple cameras can also be used to create a depth image.

Capture device100may provide the depth information and images captured by, for example, IR camera102and/or the RGB camera103, including a skeletal model and/or facial tracking model that may be generated by capture device100, where the skeletal and/or facial tracking models, depth information, and captured images are used to, for example, create a virtual screen, adapt the user interface, and control an application.

In summary, capture device100may comprise a projector unit104, a digital camera (e.g., IR camera)102, another digital camera (e.g., multi-chromatic camera)103, and a processor (controller) configured to operate capture device100according to the embodiments described herein. However, the above assembly configuration is described for illustration purposes only, and is should not be limiting to the present disclosure. Various configurations of an assembly for a 3D object acquisition may be used to implement the embodiments described herein. For example, an assembly for a 3D object acquisition configured to enable the reconstructed object distortion corrections may include three digital cameras, two of which may be used to reconstruct a 3D image of an object, and the third camera (e.g. with a resolution that is different than those of the two cameras) may be used to capture images of the object in order to identify image distortions in the reconstructed object and to compensate for identified distortions.

Real-Time Thermal Distortion Compensation

Techniques described herein are directed to the problem of real-time correction of temperature-related drifts in the intrinsic and extrinsic geometric parameters of a stereo depth camera. In one embodiment, the technique includes a thermal process that includes measuring the current temperature of the capture device (e.g., capture device100ofFIG. 1), or part thereof (e.g., IR camera, or near such a device), at one or several points, and applying a correction model on the fly to calibration parameters used in the process for generating depth or other values. In one embodiment, the process is the triangulation process. The result is a depth image with considerably diminished thermal distortions.

These techniques have a number of benefits such as passive sensing of the temperature and open loop fix on the result depth parameters. The techniques are both reasonably accurate and cost effective and can cope with a large range of ambient and self-heat temperatures.

More specifically, as discussed above, a coded light camera comprising an IR projector104projects one-dimensional code patterns onto the scene, and an IR camera102captures the patterns. Decoding of the captured patterns at every pixel location xcin the camera produces a code encoding the location xpof the projected plane. In triangulation, the plane is intersected with the ray emanating from the camera focal point through xc, yielding the distance to the object z(xc). In one embodiment, triangulation relies on two types of parameters: the intrinsic parameters of camera102and the projector104characterizing their individual optical systems (such as focal distance, principal point of the projection, and a distortion model accounting for deviations from the perfect pin-hole model), and the extrinsic parameters specifying the relative location and orientation of projector104with respect to the camera (e.g., IR camera)102.

For purposes herein, the intrinsic camera and projector parameters are collectively noted as θcand θp, respectively, while the extrinsic parameters are denoted as θe. These three sets of parameters are further collectively denoted as θ. The triangulation process can be written symbolically as
z=T(xc,xp;θ):

The parameters θ=(θc;θp;θe) are specific to a device and in one embodiment are determined by use of a calibration procedure, which is typically a part of the unit assembly process, or a global parametric model for the system.

However, during the operation of capture device100, mechanical changes due to external or internal heating may change the parameters to a degree impacting adversely the quality of depth reconstruction. For example, uneven heating of the lens may cause its vertical shift impacting the focal length, or a rotation, impacting the principal point. Furthermore, the whole assembly may bend, impacting the convergence angle between the camera (e.g., camera102) and the projector (e.g., projector104).

Techniques described herein include a way to overcome this drawback of common designs by creating a parametric model that receives, as input(s), the reading of the unit temperature at one or more points in capture device100, and produces as the output a correction of the unit intrinsic and extrinsic calibration parameters that are updated on the fly and used for triangulation. Denoting the temperature input as t, this can be written symbolically as:
Δθ=C(t;a);
where a are the model parameters and Δθ is the correction.

FIG. 2is a block diagram depicting one embodiment of an online compensation scheme for a stereo depth camera. The stereo depth camera comprise 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, and one or more temperature sensors to measure the temperature at one or more locations in the stereo camera. In one embodiment, a temperature sensor is attached to a printed circuit board (PCB) and located near the IR camera on the PCB to measure temperature at that location. In another embodiment, an additional temperature sensor is located on the PCB in the middle between the IR camera and the IR projector. In yet another embodiment, an additional temperature sensor is included near the IR projector on the PCB.

Referring toFIG. 2, an image processing pipeline301receives a sequence of images and determines depth information in response to parameters of the projector (e.g., projector104) and camera (e.g., camera102). Note that in one embodiment, image processing pipeline201ofFIG. 2is part of the capture device (e.g., the capture device ofFIG. 1). In another embodiment, the image processing pipeline ofFIG. 2is not part of the capture device and performs triangulation in a separate device.

Image processing pipeline201includes a triangulation processing unit210. Triangulation processing unit210receives xpand xc, which are well-known in the triangulation process and calibration parameters and, in response thereto, generates depth values, z, by means of triangulation. The value of xp, which corresponds to a pixel, is received as an input at the triangulation-based reconstruction module. This value includes a decoded value of a pixel location of the pixel on a projector plane, as determined from a binary code, which, in turn, is determined based on the image captured by the IR camera. The triangulation-based reconstruction process is configured to take into account the distortion due to temperature.

In one embodiment, triangulation processing unit210receives the calibration parameters from a thermal correction unit220. In one embodiment, the calibration parameters characterize an optical system formed by the projector and the first camera and comprise intrinsic parameters of the first camera (e.g., IR camera) and the projector (e.g., IR projector) and extrinsic parameters of first camera (e.g., IR camera). In one embodiment, the intrinsic parameters comprise one or more of a focal distance of an IR camera, a principal point of projection of an IR projector, and a distortion model and wherein the extrinsic parameters comprise a relative location and orientation of the IR projector with respect to the IR camera.

Thermal correction unit220is responsive to temperature information231from the one or more temperature sensors230on the capture device (e.g., the capture device ofFIG. 1). In response to the temperature information, thermal correction unit220adjusts one or more of the calibration parameters of the projector and the first camera that are provided to and used by triangulation processing unit210in generating the depth values.

In one embodiment, the calibration parameters are initialized by one or more calibration procedures and thermal correction unit220is operable to adjust one or more of these calibration parameters based on outputs from a thermal correction model242that is responsive to temperature information231from one or more temperature sensors230.

In one embodiment, thermal correction unit220receives an initial set of calibration parameters θ (250) and temperature information231indicative of the temperature of at least a portion of the capture device (e.g., the temperature near the IR camera, the temperature at a location on the printed circuit board of the capture device between the IR camera and the IR projector, the temperature near the IR projector, etc.). In one embodiment, the initial set of calibration parameters θ (250) are those calibration parameters determined during assembly or manufacturing of the capture device (e.g., prior to deployment). This set of calibration parameters are set under specific temperature conditions that existed during the initial calibration process (i.e., the temperature at which the capture device is calibrated).

In one embodiment, thermal correction model242is device-specific and can be generated by testing a device under different temperatures and recording changes for each individual calibration parameters (Δθ) for each specific change in temperature. For example, a capture device could be attached to a thermo-electric controller and be subjected to a variety of different temperatures to see their effect on the cameras and/or projectors in the device, with focus on the parameters that are based on the depth value generation process (e.g., triangulation). Once all the changes have been recorded, a function can be defined or identified that models the behavior of the device under those different temperature conditions. In one embodiment, graphs are generated that plot changes to each parameter versus the change in temperature (from the nominal temperature under which the capture device was originally calibrated). For each plot, a function that best represents the points in the plot is chosen. For example, in one embodiment, a linear model may represent the behavior of a particular parameter under different temperatures. In such a case, a coefficient α associated with generating a Δθ for that parameter may be set equal to the slope of the line representing the linear model. This may be shown with the following equation:
Δθ1(t)=α1(t−t0)
where a1is a coefficient representing the effect of temperature on the parameter θ1(e.g., the slope of the linear function modeling the behavior of the parameter θ1for various temperatures) from the difference in temperature between the current temperature t and temperature t0, which is the nominal temperature under which the initial parameter θ1use set and determined during initial calibration. While this example uses a linear model, the generation of Δθ values for compensation maybe based on non-linear models, at least in part because these values are device and implementation dependent.

Note the formula above only mentions one coefficient; however, in general, there will be a vector α containing a number of coefficients, one for each parameter that is to undergo compensation due to temperature.

In one embodiment, taking temperature measurements and the updating of Δθ values occurs every frame according to the frame rate. Thus, changes identified during one frame are used in processing the next fame.

FIG. 3is a flow diagram of one embodiment of a process for performing on-line temperature compensation for thermal distortions in a stereo depth camera. The process is performed by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine), firmware, or a combination of these three.

Referring toFIG. 3, the process begins by projecting a sequence of light patterns on an object using a projector (processing block301). In one embodiment, this is performed by projector104(e.g., an IR projector) ofFIG. 1.

A camera captures a sequence of images of the object illuminated with the projected light patterns (processing block302). In one embodiment, the camera is camera102(e.g., an IR camera) ofFIG. 1.

From the captured sequence of images, processing logic determines depth information using calibration parameters of the projector and camera, including adjusting, based on temperature information from one or more temperature sensors, one or more of the calibration parameters of the projector and the first camera (processing block303). In one embodiment, the calibration parameters characterize an optical system formed by the projector and the first camera and comprise intrinsic parameters of the first camera and the projector and extrinsic parameters of first camera. In one embodiment, the intrinsic parameters comprise one or more of a focal distance of the first camera, a principal point of projection of the projector, and a distortion model and wherein the extrinsic parameters comprise a relative location and orientation of the projector with respect to the first camera.

In on embodiment, determining depth information comprises performing a triangulation process using the calibration parameters. In one embodiment, the calibration parameters are initialized by one or more calibration procedures and adjusting calibration parameters of the projector and the camera is based on outputs from a thermal correction model that is responsive to the temperature information from the one or more temperature sensors.

FIG. 3described above refers only to a time-multiplexed structural (coded) light system. The technologies described herein are applicable to other systems and ways of operation (e.g., passive stereo). For example, such systems may include passive stereo: 1) passive stereo systems comprising two (or more) calibrated cameras; 2) assisted stereo—as above plus an uncalibrated projector projecting some type of random noise onto the scene to improve correspondence computation between the cameras; and 3) coded light system comprising one (or more) calibrated camera and a calibrated projector projecting code. The latter code can be spatially multiplexed (single pattern), temporally multiplexed (multiple patterns), or both. The code can be one dimensional (encoding planes) or two-dimensional (encoding rays) or a combination of the two.

A Triangulation Process with an Exemplary Forward Model

Pattern Camera

Given a point x in world coordinates, it is projected onto the camera image plane by the following pin-hole model:
x′c=Kc(10)x(1)
where xcis a 4×1 vector representing the world point location in homogenous coordinates, Kcis the 3×3 upper-triangular intrinsic camera matrix,

Kc=(cxfxfycy1),(2)
and xcis the 3×1 vector of uncorrected homogenous camera coordinates. The camera system of coordinates is assumed to coincide with the world coordinate system, therefore trivial rotation and translation (extrinsic) matrices are imposed.
Pattern Camera Lens Distortion

To account for the lens distortion, a parametric distortion model is applied. The corrected camera coordinates vector xcis related to its uncorrected counterpart x′cthrough the following inverse model given by
x′c=KcDα(Kc−1xc)  (3)
where D is a plane-to-plane map given by
x′=(1+α1p2+α2p4+α3p6)x+2α4ry+α5(p2+2x2)
y′=(1+α1p2+α2p4+α3p6)y+2α5ry+α4(p2+2y2)  (4)
with p2=x′2+y′2. The parameters α1, α2, α3govern the radial distortion component, while α4and α5account for the tangential one. The vector α=(α1, . . . α5)Tis referred to herein as to the camera inverse distortion parameters. Note that the difference between this inverse model versus the standard forward model used in other systems. The benefits of the teachings described herein will be evident when the reconstruction process is disclosed.
Projector

The projector is modeled as a one-dimensional pin-hole system
xp=Kp(Rptp)x=Ptx,(5)
where xpis the 2×1 vector of homogenous coordinate on the projector line, Kpis the 2×3 intrinsic matrix, and Rpand tpare the extrinsic rotation and translation transformation Parameters, respectively, relating between the camera and the projector coordinate systems. In one embodiment, the intrinsic and the extrinsic projector parameters are combined into a single 2×4 projection matrix Pp.
Texture Camera

The texture camera is modeled as a two-dimensional pin-hole system
x′t=Kt(Rttt)x=Ptx,(6)
where xtis the 3×1 uncorrected vector of homogenous coordinate on the camera plane, Ktis the 3×3 intrinsic matrix, and Rtand ttare the extrinsic rotation and translation transformation parameters, respectively, relating between the pattern and the texture cameras coordinate systems. In one embodiment, the intrinsic and the extrinsic texture camera parameters are combined into a single 3×4 projection matrix Pt.
Texture Camera Lens Distortion.

To account for the lens distortion, a forward parametric distortion model is applied:
xt=KtDβ(Kt−1x′t)  (7)
where D is a plane-to-plane map given by
x=(1+β1p2+β2p4+β3p6)x′+2β4x′y′+β5(p2+2x′2)
y=(1+β1p2+β2p4+β3p6)y′+2β5x′y′αβ4(p2+2y′2)  (8)
with p2=x′2+y′2. The parameters β1, β2, β3govern the radial distortion component, while β4and β5account for the tangential one. The vector β=(β1, . . . β5)Tis referred to herein as the texture camera forward distortion parameters. The forward model is applied by transforming directly the texture image using Dβ. In one embodiment, the process is performed on the host system due to the requirement of random access to the frame buffer.
Depth Reconstruction

The purpose of depth reconstruction is to establish for each pixel in the pattern camera image its distance from the camera center of coordinates. The term “depth” shall be understood as either the distance r or its projection z onto the optical axis of the pattern camera.

Given a pixel at position xc=(xc, yc, 1)Tin the camera image plane and the corresponding code value xp=(xp, 1)Tof the projector, by applying the inverse distortion model (7), a corrected pixel location x′c=(x′c, y′c, 1)Tis obtained. The pinhone camera projection (1) introduces ambiguity by projecting all points along the ray x=rd, r>0, to x′c.

In order to characterize the ray d=(dx; dy; dz)T, the following is written
βx′c=Kcd,(9)
where β≠0 is an arbitrary scaling factor. This yields
dzx′c=(Kc)1Td=fxdx+cxdz
dzy′c=(Kc)2Td=fydy+cydz(10)
where (Kc)iTdenotes the i-th row vector of Kc. Solving for dx; dyand dzgives

d=(fx-1⁡(xc′-cx)fy-1⁡(yc′-cy)1)(11)
Since the pixel grid is fixed at reconstruction, the described process is precomputed for every pixel in the image.

The projector forward model (6) introduces ambiguity by projecting an entire plane onto xp; however, the intersection of the latter plane with the ray x=rd yields a unique point in space. Substituting the unit direction d=d/∥d∥ into the projector model gives
βxp=Pp(1rd),  (12)
which can be rewritten as

β⁢⁢xp=rp1T⁢d+1⁢q1rp2T⁢d+1⁢q2=rp1T⁢d+1⁢q1⁢drp2T⁢d+1⁢q2⁢d(13)
where the projector matrix is decomposed into

Pp=(p1T⁢q1p2T⁢q2).(14)
Solving for r, one has the depth

r=T1⁢xp+T2T3⁢xp+T4(15)
where
T1=q2∥d∥
T2=q1∥d∥
T3=p2Td
T4=p1Td(16)
are scalar coefficients depending only on the pixel location xc(and therefore can be precomputed). For purposes herein T1, . . . T4are referred to as to range triangulation coefficients. Note that using the unnormalized ray saves one division operation.

The depth r can be further projected onto dzyielding

z=⁢T1⁢xp+T2⁢dzT3⁢xp+T4⁢d=⁢T1′⁢xp+T2′T3⁢xp+T4(17)
where
T′1=q2dz
T′2=q1dz(18)
are the modified coefficients.
Texture Map Reconstruction

Once the range has been established, the texture map coordinates are reconstructed using the forward model
xt=Pt(1rd)=Pt(∥d∥rd)  (19)
from where

In case the output depth is projected onto the camera optical axis, texture coordinates are obtained from r=z∥d∥/dz,

yt=zp2T⁢d+q2⁢dzzp3T⁢d+q3⁢dz(24)
This can be expressed as (21) with σ4, σ5replaced by σ6replaced by
σ′4=q1dz
σ′5=q2dz
σ′6=q3dz.  (25)
Coefficient Scaling and Precomputation

In the following, a compact and efficient way to compute the coefficients Tiand σiis disclosed. It assumes the pattern camera coordinates to be unnormalized and translated from [−1, 1]2to [0,W−1]×[0,H−1]; the projector coordinate is assumed to be translated from [−1, 1] to [0; s2−1] (s2=215−1 in one embodiment); the output range or depth is mapped from the interval [0; rmax] to [0, 216−1] by the scaling factor s1; and the texture map coordinates are computed translated from [−1, 1] to [0, suv] with suv=212−1 in one embodiment.

Inverse Camera Distortion Model.

Substituting the scales into the camera distortion model, the following is obtained with appropriate scaling

x=2Wfx⁢xc-H2⁢fyfx⁢(cx+1)⁢⁢y=yc-H2⁢(cy+1).(26)
The distortion model becomes
x′=(1+d1p2+d2p4+d3p6)x+2d4xy+d5(p2+2x2)
y′=(1+d1p2+d2p4+d3p6)y+2d5xy+d4(p2+2y2)  (27)
p2=x2+y2and diare the distortion parameters scaled according to

d1=(2Hfy)2⁢α1⁢⁢d2=(2Hfy)4⁢α2⁢⁢d3=α3⁢⁢d4=(2Hfy)2⁢α4⁢⁢d5=(2Hfy)2⁢α5.(28)
Ray Direction

With the selected scaling, the unnormalized ray d passing through the pixel becomes

d=(x′,y′,Hfy2),(29)
and its norm
∥d∥=√{square root over (x)}′2+y′2+q,(30)
where

q=(Hfy2)2(31)
Range Coefficients

In one embodiment, range coefficients assume the form
T1=p1∥d∥
T2=p2∥d∥
T3=p3x′+p4y′+p5
T4=p6x′+p7y′+p8(32)
where

p1=(Pp)24⁢s1⁢⁢p2=-s1⁢s2⁡((Pp)14+(Pp)24)⁢⁢p3=-(Pp)21⁢⁢p4=-(Pp)22⁢⁢p5=-(Pp)23⁢H2⁢fy⁢⁢p6=s2⁡((Pp)11+(Pp)21)⁢⁢p7=s2⁡((Pp)12+(Pp)22)⁢⁢p8=s2⁡((Pp)13+(Pp)23)⁢H2⁢fy(33)
are precomputed from the system parameters. Depth reconstruction is obtained with
T′1=p′1
T′2=p′2(34)
where
p′1=p′1√{square root over (q)}
p′2=p′2√{square root over (q)}.(35)
Texture Map Coefficients

h0=(Pt)12+(Pt)32(Pt)11+(Pt)31⁢⁢h1=(Pt)13+(Pt)33(Pt)11+(Pt)31⁢H2⁢fy⁢⁢h2=(Pt)21+(Pt)31(Pt)11+(Pt)31⁢⁢h3=(Pt)22+(Pt)32(Pt)11+(Pt)31⁢⁢h4=(Pt)23+(Pt)33(Pt)11+(Pt)31⁢H2⁢fy⁢⁢h5=(Pt)31(Pt)11+(Pt)31⁢2suv⁢⁢h6=(Pt)32(Pt)11+(Pt)31⁢2suv⁢⁢h7=(Pt)33(Pt)11+(Pt)31⁢Hsuv⁢fy⁢⁢h8=(Pt)14+(Pt)34(Pt)11+(Pt)31⁢H2⁢s1⁢⁢h9=(Pt)24+(Pt)34(Pt)11+(Pt)31⁢s1⁢⁢h10=2⁢(Pt)34(Pt)11+(Pt)31⁢s1suv.(37)
When texture map is reconstructed from z rather than from r,
σ′4=h′8
σ′5=h′9
σ′6=h′10(38)
are used with
h′8=h8√{square root over (q)}
h′9=h9√{square root over (q)}
h′10=h10√{square root over (q)}(39)

FIG. 4illustrates an example of coefficient precomputation.FIG. 5illustrates an example of range and texture reconstruction and coefficient precomputation.

FIG. 6illustrates, for one embodiment, an example system600having one or more processor(s)604, system control module608coupled to at least one of the processor(s)604, system memory612coupled to system control module608, non-volatile memory (NVM)/storage614coupled to system control module608, and one or more communications interface(s)620coupled to system control module608. In some embodiments, the system600may include capture device100and provide logic/module that performs functions aimed at compensating for temperature in the depth determination and correcting distortions in a reconstructed object image described herein.

In some embodiments, the system600may include one or more computer-readable media (e.g., system memory or NVM/storage614) having instructions and one or more processors (e.g., processor(s)604) coupled with the one or more computer-readable media and configured to execute the instructions to implement a module to perform image distortion correction calculation actions described herein.

System control module608for one embodiment may include any suitable interface controllers to provide for any suitable interface to at least one of the processor(s)604and/or to any suitable device or component in communication with system control module608.

System control module608may include memory controller module610to provide an interface to system memory612. The memory controller module610may be a hardware module, a software module, and/or a firmware module. System memory612may be used to load and store data and/or instructions, for example, for system600. System memory612for one embodiment may include any suitable volatile memory, such as suitable DRAM, for example. System control module608for one embodiment may include one or more input/output (I/O) controller(s) to provide an interface to NVM/storage614and communications interface(s)620.

The NVM/storage614may be used to store data and/or instructions, for example. NVM/storage614may include any suitable non-volatile memory, such as flash memory, for example, and/or may include any suitable non-volatile storage device(s), such as one or more hard disk drive(s) (HDD(s)), one or more compact disc (CD) drive(s), and/or one or more digital versatile disc (DVD) drive(s), for example. The NVM/storage614may include a storage resource physically part of a device on which the system600is installed or it may be accessible by, but not necessarily a part of, the device. For example, the NVM/storage614may be accessed over a network via the communications interface(s)620.

Communications interface(s)620may provide an interface for system600to communicate over one or more network(s) and/or with any other suitable device. The system600may wirelessly communicate with the one or more components of the wireless network in accordance with any of one or more wireless network standards and/or protocols.

For one embodiment, at least one of the processor(s)604may be packaged together with logic for one or more controller(s) of system control module608, e.g., memory controller module610. For one embodiment, at least one of the processor(s)604may be packaged together with logic for one or more controllers of system control module608to form a System in Package (SiP). For one embodiment, at least one of the processor(s)604may be integrated on the same die with logic for one or more controller(s) of system control module608. For one embodiment, at least one of the processor(s)604may be integrated on the same die with logic for one or more controller(s) of system control module608to form a System on Chip (SoC).

In various embodiments, the system600may have more or less components, and/or different architectures. For example, in some embodiments, the system600may include one or more of a camera, a keyboard, liquid crystal display (LCD) screen (including touch screen displays), non-volatile memory port, multiple antennas, graphics chip, application-specific integrated circuit (ASIC), and speakers.

In various implementations, the system600may be, but is not limited to, a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet, a netbook, etc.), a laptop, a netbook, a notebook, an ultrabook, a smartphone, a tablet, a personal digital assistant (PDA), an ultra mobile PC, a mobile phone, a desktop computer, a server, a printer, a scanner, a monitor, a set-top box, an entertainment control unit, a digital camera, a portable music player, or a digital video recorder. In further implementations, the system600may be any other electronic device.

FIG. 7illustrates an embodiment of a computing environment700capable of supporting the operations discussed above. The modules described before can use the depth information (e.g., values) and other data described above to perform these functions. The modules and systems can be implemented in a variety of different hardware architectures and form factors.

Command Execution Module701includes a central processing unit to cache and execute commands and to distribute tasks among the other modules and systems shown. It may include an instruction stack, a cache memory to store intermediate and final results, and mass memory to store applications and operating systems. Command Execution Module701may also serve as a central coordination and task allocation unit for the system.

Screen Rendering Module721draws objects on the one or more multiple screens for the user to see. It can be adapted to receive the data from Virtual Object Behavior Module704, described below, and to render the virtual object and any other objects and forces on the appropriate screen or screens. Thus, the data from Virtual Object Behavior Module704would determine the position and dynamics of the virtual object and associated gestures, forces and objects, for example, and Screen Rendering Module721would depict the virtual object and associated objects and environment on a screen, accordingly. Screen Rendering Module721could further be adapted to receive data from Adjacent Screen Perspective Module707, described below, to either depict a target landing area for the virtual object if the virtual object could be moved to the display of the device with which Adjacent Screen Perspective Module707is associated. Thus, for example, if the virtual object is being moved from a main screen to an auxiliary screen, Adjacent Screen Perspective Module707could send data to the Screen Rendering Module721to suggest, for example in shadow form, one or more target landing areas for the virtual object on that track to a user's hand movements or eye movements.

Object and Gesture Recognition System722may be adapted to recognize and track hand and harm gestures of a user. Such a module may be used to recognize hands, fingers, finger gestures, hand movements and a location of hands relative to displays. For example, Object and Gesture Recognition System722could for example determine that a user made a body part gesture to drop or throw a virtual object onto one or the other of the multiple screens, or that the user made a body part gesture to move the virtual object to a bezel of one or the other of the multiple screens. Object and Gesture Recognition System722may be coupled to a camera or camera array, a microphone or microphone array, a touch screen or touch surface, or a pointing device, or some combination of these items, to detect gestures and commands from the user.

The touch screen or touch surface of Object and Gesture Recognition System722may include a touch screen sensor. Data from the sensor may be fed to hardware, software, firmware or a combination of the same to map the touch gesture of a user's hand on the screen or surface to a corresponding dynamic behavior of a virtual object. The sensor date may be used to momentum and inertia factors to allow a variety of momentum behavior for a virtual object based on input from the user's hand, such as a swipe rate of a user's finger relative to the screen. Pinching gestures may be interpreted as a command to lift a virtual object from the display screen, or to begin generating a virtual binding associated with the virtual object or to zoom in or out on a display. Similar commands may be generated by Object and Gesture Recognition System722, using one or more cameras, without the benefit of a touch surface.

Direction of Attention Module723may be equipped with cameras or other sensors to track the position or orientation of a user's face or hands. When a gesture or voice command is issued, the system can determine the appropriate screen for the gesture. In one example, a camera is mounted near each display to detect whether the user is facing that display. If so, then the direction of attention module information is provided to Object and Gesture Recognition Module722to ensure that the gestures or commands are associated with the appropriate library for the active display. Similarly, if the user is looking away from all of the screens, then commands can be ignored.

Device Proximity Detection Module725can use proximity sensors, compasses, GPS (global positioning system) receivers, personal area network radios, and other types of sensors, together with triangulation and other techniques to determine the proximity of other devices. Once a nearby device is detected, it can be registered to the system and its type can be determined as an input device or a display device or both. For an input device, received data may then be applied to Object Gesture and Recognition System722. For a display device, it may be considered by Adjacent Screen Perspective Module707.

Virtual Object Behavior Module704is adapted to receive input from Object Velocity and Direction Module703, and to apply such input to a virtual object being shown in the display. Thus, for example, Object and Gesture Recognition System722would interpret a user gesture and by mapping the captured movements of a user's hand to recognized movements, Virtual Object Tracker Module706would associate the virtual object's position and movements to the movements as recognized by Object and Gesture Recognition System722, Object and Velocity and Direction Module703would capture the dynamics of the virtual object's movements, and Virtual Object Behavior Module704would receive the input from Object and Velocity and Direction Module703to generate data that would direct the movements of the virtual object to correspond to the input from Object and Velocity and Direction Module703.

Virtual Object Tracker Module706on the other hand may be adapted to track where a virtual object should be located in three-dimensional space in a vicinity of a display, and which body part of the user is holding the virtual object, based on input from Object Gesture and Recognition System722. Virtual Object Tracker Module706may for example track a virtual object as it moves across and between screens and track which body part of the user is holding that virtual object. Tracking the body part that is holding the virtual object allows a continuous awareness of the body part's air movements, and thus an eventual awareness as to whether the virtual object has been released onto one or more screens.

Gesture to View and Screen Synchronization Module708, receives the selection of the view and screen or both from Direction of Attention Module723and, in some cases, voice commands to determine which view is the active view and which screen is the active screen. It then causes the relevant gesture library to be loaded for Object and Gesture Recognition System722. Various views of an application on one or more screens can be associated with alternative gesture libraries or a set of gesture templates for a given view.

Adjacent Screen Perspective Module707, which may include or be coupled to Device Proximity Detection Module725, may be adapted to determine an angle and position of one display relative to another display. A projected display includes, for example, an image projected onto a wall or screen. The ability to detect a proximity of a nearby screen and a corresponding angle or orientation of a display projected therefrom may for example be accomplished with either an infrared emitter and receiver, or electromagnetic or photo-detection sensing capability. For technologies that allow projected displays with touch input, the incoming video can be analyzed to determine the position of a projected display and to correct for the distortion caused by displaying at an angle. An accelerometer, magnetometer, compass, or camera can be used to determine the angle at which a device is being held while infrared emitters and cameras could allow the orientation of the screen device to be determined in relation to the sensors on an adjacent device. Adjacent Screen Perspective Module707may, in this way, determine coordinates of an adjacent screen relative to its own screen coordinates. Thus, the Adjacent Screen Perspective Module may determine which devices are in proximity to each other, and further potential targets for moving one or more virtual object's across screens. Adjacent Screen Perspective Module707may further allow the position of the screens to be correlated to a model of three-dimensional space representing all of the existing objects and virtual objects.

Object and Velocity and Direction Module703may be adapted to estimate the dynamics of a virtual object being moved, such as its trajectory, velocity (whether linear or angular), momentum (whether linear or angular), etc. by receiving input from Virtual Object Tracker Module706. The Object and Velocity and Direction Module703may further be adapted to estimate dynamics of any physics forces, by for example estimating the acceleration, deflection, degree of stretching of a virtual binding, etc. and the dynamic behavior of a virtual object once released by a user's body part. Object and Velocity and Direction Module703may also use image motion, size and angle changes to estimate the velocity of objects, such as the velocity of hands and fingers

Momentum and Inertia Module702can use image motion, image size, and angle changes of objects in the image plane or in a three-dimensional space to estimate the velocity and direction of objects in the space or on a display. Momentum and Inertia Module702is coupled to Object and Gesture Recognition System722to estimate the velocity of gestures performed by hands, fingers, and other body parts and then to apply those estimates to determine momentum and velocities to virtual objects that are to be affected by the gesture.

3D Image Interaction and Effects Module705tracks user interaction with 3D images that appear to extend out of one or more screens. The influence of objects in the z-axis (towards and away from the plane of the screen) can be calculated together with the relative influence of these objects upon each other. For example, an object thrown by a user gesture can be influenced by 3D objects in the foreground before the virtual object arrives at the plane of the screen. These objects may change the direction or velocity of the projectile or destroy it entirely. The object can be rendered by the 3D Image Interaction and Effects Module705in the foreground on one or more of the displays.

In a first example embodiment, an apparatus comprises a first camera configured to capture a sequence of images of the object, a second device comprising a projector or a camera, 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.

In another example embodiment, the subject matter of the first example embodiment can optionally include that the second device comprises a projector, and further wherein the calibration parameters characterize an optical system formed by the projector and the first camera and comprise intrinsic parameters of the first camera and the projector and extrinsic parameters of first camera. In another example embodiment, the subject matter of the last example embodiment can optionally include that the intrinsic parameters comprise one or more of a focal distance of the first camera, a principal point of projection of the projector, and a distortion model and wherein the extrinsic parameters comprise a relative location and orientation of the projector with respect to the first camera.

In another example embodiment, the subject matter of the first example embodiment can optionally include that the processing unit is configured to perform a triangulation process using the calibration parameters. In another example embodiment, the subject matter of the last example embodiment can optionally include that the calibration parameters are initialized by one or more calibration procedures and further wherein the thermal correction unit is operable to adjust one or more of the calibration parameters based on outputs from a thermal correction model that is responsive to the temperature information from the one or more temperature sensors.

In another example embodiment, the subject matter of the first example embodiment can optionally include that the first camera comprises a digital infrared camera.

In another example embodiment, the subject matter of the first example embodiment can optionally include that the second device comprises a projector configured to project a sequence of light patterns on an object.

In another example embodiment, the subject matter of the first example embodiment can optionally include that the projector is configured to project one-dimensional coded light patterns on the object.

In another example embodiment, the subject matter of the first example embodiment can optionally include a second camera configured to capture a second image of the object illuminated with the projected light patterns and configured to capture a texture image of the object in the second image. In another example embodiment, the subject matter of the first example embodiment can optionally include that the second device comprises an infrared (IR) projector, the first camera comprises an IR camera, and the second camera comprises a red, green, and blue (RGB) camera.

In a second example embodiment, a method comprises capturing, using a first camera, a sequence of images of the object; and determining depth information using calibration parameters of the camera and a second device, including adjusting, based on temperature information from one or more temperature sensors, one or more of the calibration parameters of the first camera and the second device, where the second device comprises a projector or a camera.

In another example embodiment, the subject matter of the second example embodiment can optionally include that the second device comprises a projector that projects the light patterns and further wherein the calibration parameters characterize an optical system formed by the projector and the first camera and comprise intrinsic parameters of the first camera and the projector and extrinsic parameters of first camera.

In another example embodiment, the subject matter of the second example embodiment can optionally include that the intrinsic parameters comprise one or more of a focal distance of the first camera, a principal point of projection of the projector, and a distortion model and wherein the extrinsic parameters comprise a relative location and orientation of the projector with respect to the first camera.

In another example embodiment, the subject matter of the second example embodiment can optionally include that determining depth information comprises performing a triangulation process using the calibration parameters. In another example embodiment, the subject matter of the last example embodiment can optionally include projecting, using a projector, a sequence of light patterns on an object, and wherein the calibration parameters are initialized by one or more calibration procedures and further wherein adjusting the one or more of the calibration parameters of the projector and the camera is based on outputs from a thermal correction model that is responsive to the temperature information from the one or more temperature sensors.

In a third example embodiment, an article of manufacture has one or more non-transitory computer readable storage media storing instructions which when executed by a system to perform a method comprises: projecting a sequence of light patterns on an object; capturing a sequence of images of the object illuminated with the projected light patterns; determining depth information in response to parameters of the projector and camera; and adjusting, based on temperature information from one or more temperature sensors, one or more of the calibration parameters of the projector and the camera.

In another example embodiment, the subject matter of the third example embodiment can optionally include that the calibration parameters characterize an optical system formed by the projector and the first camera and comprise intrinsic parameters of the first camera and the projector and extrinsic parameters of first camera.

In another example embodiment, the subject matter of the last example embodiment can optionally include that the intrinsic parameters comprise one or more of a focal distance of the first camera, a principal point of projection of the projector, and a distortion model and wherein the extrinsic parameters comprise a relative location and orientation of the projector with respect to the first camera.

In another example embodiment, the subject matter of the third example embodiment can optionally include that determining depth information comprises performing a triangulation process using the calibration parameters.

In another example embodiment, the subject matter of the third example embodiment can optionally include that the calibration parameters are initialized by one or more calibration procedures and further wherein adjusting the one or more of the calibration parameters of the projector and the camera is based on outputs from a thermal correction model that is responsive to the temperature information from the one or more temperature sensors.