Patent Publication Number: US-11659296-B2

Title: Systems and methods for structured light depth computation using single photon avalanche diodes

Description:
BACKGROUND 
     Depth maps include information indicating distances between a viewpoint (e.g., a viewpoint of a camera capturing an image of an environment) and surfaces of objects within a scene (e.g., objects within the environment captured by the camera). Depth maps are used as a tool to facilitate various computer vision tasks and/or user experiences, such as simultaneous localization and mapping, object tracking, passthrough imaging (e.g., capturing images and using a depth map to reproject them to correspond to another perspective), photogrammetry, surface reconstruction, and/or others. 
     Depth maps can be generated in various ways. One example technique for generating a depth map includes structured light depth imaging. One form of structured light depth imaging includes projecting a series of different light patterns (e.g., striped patterns with different numbers and/or widths of stripes) into an environment and utilizing a camera to capture a series of images of the environment. Each image captures the environment while the environment is illuminated with a different light pattern. 
     While capturing the images, the camera is typically kept stationary so that each image sensing pixel of the camera captures the same portion of the environment throughout the projecting and capturing of the different light patterns. 
     The light patterns of the series of different light patterns are selected to allow pixel signatures to be determined for each image sensing pixels of the camera. For example, the series of different light patterns may include arrangements of vertical stripes formed from illuminated vertical sections and unilluminated vertical sections arranged in an alternating pattern. The different light patterns may include different densities of vertical stripes. For instance, a first light pattern may include a single illuminated vertical section (occupying half of the pattern) and a single unilluminated vertical section (occupying the other half of the pattern). A second light pattern may include four vertical stripes (two illuminated, two unilluminated, arranged in an alternating pattern), a third light pattern may include eight vertical stripes (four illuminated, four unilluminated, arranged in an alternating pattern), and so forth. 
     According to the above example, a pixel signature for a particular image sensing pixel may be defined by tracking whether the portion of the environment captured by the particular image sensing pixel was illuminated during projection of each of the different light patterns into the environment. For instance, a first value may be recorded indicating whether the particular image sensing pixel detected light pattern illumination while the first light pattern was projected into the environment (e.g., a binary “1” or “0”, with a “1” indicating that illumination was detected), a second value may be recorded indicating whether the particular image sensing pixel detected light pattern illumination while the second light pattern was projected into the environment, a third value may be recorded indicating whether the particular image sensing pixel detected light pattern illumination while the third light pattern was projected into the environment, and so forth. Stated differently, the first value may indicate whether the portion of the environment captured by the particular image sensing pixel was within an illuminated stripe of the first pattern, the second value may indicate whether the portion of the environment captured by the particular image sensing pixel was within an illuminated stripe of the second pattern, the third value may indicate whether the portion of the environment captured by the particular image sensing pixel was within an illuminated stripe of the third pattern, and so forth. The various values for the particular image sensing pixel (e.g., indicating light pattern illumination or non-illumination during projection of the series of different light patterns) may be combined (e.g., arranged as a sequence of values) to generate the pixel signature for the particular image sensing pixel. 
     Pixels signatures as discussed above may be determined for the image sensing pixel of the camera based on images captured of the various structured light patterns projected into the environment. A system may also determine virtual pixel signatures based on the structured light patterns projected into the environment. For example, the virtual pixel signatures may each be associated with pixel coordinates and may be based on whether the pixel coordinates correspond to an illuminated portion or an unilluminated portion across multiple patterns. 
     Accordingly, to perform depth computations, a system may search along scanlines (e.g., horizontal scanlines, or a scanning direction orthogonal to the stripes of the structured light patterns) to identify the locations of camera pixels associated with pixel signatures that match a virtual pixel signature on the same scanline. The system may use pixel coordinates associated with matched pixel signatures and virtual pixel signatures to determine disparity and/or depth values. Such matching may be performed or attempted to calculate depth values for all camera pixels, thereby forming a depth map of the captured environment. To facilitate ideal structured light depth imaging as discussed above, the light patterns of the series of structured light patterns should be selected to allow pixel signatures for each image sensing pixel of the camera to be unique from other pixel signatures at least along the same scanline (e.g., along the same horizontal scanline). 
     In some instances, structured light imaging as described in the above example may provide high-precision depth information for a captured environment with reduced error compared to other conventional techniques for depth imaging (e.g., using a single dot pattern projection to facilitate active stereo imaging). However, environments that include moving objects present many challenges for structured light imaging utilizing a series of light patterns as discussed above. For example, structured light images are typically captured using complementary metal-oxide-semiconductor (CMOS) and/or charge-coupled device (CCD) image sensors. Such sensors may include image sensing pixel arrays where each pixel is configured to generate electron-hole pairs in response to detected photons. The electrons may become stored in per-pixel capacitors, and the charge stored in the capacitors may be read out to provide image data (e.g., by converting the stored charge to a voltage). 
     CMOS and/or CCD image sensors typically operate by performing an exposure operation to allow charge to collect in the per-pixel capacitors and subsequently performing a readout operation to generate image data based on the collected per-pixel charge. Thus, moving objects in a captured environment often occupy one position (or one set of positions) during exposure and/or readout of a first light pattern image and occupy a different position (or different set of positions) during exposure and/or readout of a second light pattern image. Thus, different light pattern images may include spatially misaligned representations of the same objects in the environment, which can cause errors in depth computations. 
     Thus, for at least the foregoing reasons, there is an ongoing need and desire for improved systems and methods for facilitating structured light depth computations. 
     The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one exemplary technology area where some embodiments described herein may be practiced. 
     BRIEF SUMMARY 
     Disclosed embodiments include systems, methods, and devices for facilitating structured light depth computation using single photon avalanche diodes (SPADs). 
     Some embodiments provide a system that includes a SPAD array comprising a plurality of SPAD pixels, an illuminator configured to emit one or more structured light patterns, one or more processors, and one or more hardware storage devices storing instructions that are executable by the one or more processors to configure the system to perform various acts. The acts include, over a frame capture time period, selectively activating the illuminator to perform interleaved structured light illumination operations. The interleaved structured light illumination operations include alternately emitting at least a first structured light pattern from the illuminator and emitting at least a second structured light pattern from the illuminator. The acts also include, over the frame capture time period, performing a plurality of sequential shutter operations to configure each SPAD pixel of the SPAD array to enable photon detection. The plurality of sequential shutter operations generates, for each SPAD pixel of the SPAD array, a plurality of binary counts indicating whether a photon was detected during each of the plurality of sequential shutter operations. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
     Additional features and advantages will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the teachings herein. Features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. Features of the present invention will become more fully apparent from the following description and appended claims or may be learned by the practice of the invention as set forth hereinafter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order to describe the manner in which the above-recited and other advantages and features can be obtained, a more particular description of the subject matter briefly described above will be rendered by reference to specific embodiments which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments and are not therefore to be considered to be limiting in scope, embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which: 
         FIG.  1    illustrates example components of an example system that may include or be used to implement one or more disclosed embodiments; 
         FIG.  2    illustrates an example implementations of single photon avalanche diode (SPAD) sensors in a head-mounted display (HMD); 
         FIGS.  3 A- 3 E  illustrate an example of SPAD exposure operations performed over a frame capture time period under illumination by different structured light patterns; 
         FIG.  3 F  illustrates example images generated from binary counts obtained via shutter operations performed over the frame capture time period; 
         FIGS.  4 A- 4 C and  5 A- 5 B  illustrate examples of determining a pixel signature associated with a SPAD pixel; 
         FIG.  6    illustrates an example of determining a virtual pixel signature for a virtual; 
         FIG.  7    illustrates an example of generating a disparity value based on a pixel signature and a corresponding virtual pixel signature; and 
         FIG.  8    illustrates an example flow diagram depicting acts associated with structured light depth computation using SPADs. 
     
    
    
     DETAILED DESCRIPTION 
     Disclosed embodiments are generally directed to systems, methods, and devices for facilitating structured light depth computation using single photon avalanche diodes (SPADs). 
     Examples of Technical Benefits, Improvements, and Practical Applications 
     Those skilled in the art will recognize, in view of the present disclosure, that at least some of the disclosed embodiments may be implemented to address various shortcomings associated with at least some conventional structured light depth computation techniques. The following section outlines some example improvements and/or practical applications provided by the disclosed embodiments. It will be appreciated, however, that the following are examples only and that the embodiments described herein are in no way limited to the example improvements discussed herein. 
     In contrast with conventional CMOS or CCD sensors, a SPAD is operated at a bias voltage that enables the SPAD to detect a single photon. Upon detecting a single photon, an electron-hole pair is formed, and the electron is accelerated across a high electric field, causing avalanche multiplication (e.g., generating additional electron-hole pairs). Thus, each detected photon may trigger an avalanche event. A SPAD may operate in a gated manner (each gate corresponding to a separate shutter operation), where each gated shutter operation may be configured to result in a binary output. The binary output may comprise a “1” where an avalanche event was detected during an exposure (e.g., where a photon was detected), or a “0” where no avalanche event was detected. 
     Separate shutter operations may be integrated over a frame capture time period. The binary output of the shutter operations over a frame capture time period may be counted, and an intensity value may be calculated based on the counted binary output. 
     An array of SPADs may form an image sensor, with each SPAD forming a separate pixel in the SPAD array. To capture an image of an environment, each SPAD pixel may detect avalanche events and provide binary output for consecutive shutter operations in the manner described herein. The per-pixel binary output of multiple shutter operations over a frame capture time period may be counted, and per-pixel intensity values may be calculated based on the counted per-pixel binary output. The per-pixel intensity values may be used to form an intensity image of an environment. 
     As will be described in more detail hereinafter, techniques of the present disclosure include performing interleaved light pattern exposure operations using SPAD pixels of a SPAD array. During a first light pattern exposure, sequential shutter operations are performed using the SPAD pixels to generate binary counts, with each binary count indicating whether a photon was detected during a shutter operation (e.g., “1” indicating a photon was detected, “0” indicating that no photon was detected). The sequential shutter operations of the first light pattern exposure are performed as the captured scene is illuminated according to a first light pattern. During a second light pattern exposure, sequential shutter operations are performed using the SPAD pixels while an illuminator emits a second light pattern into the captured scene. These shutter operations also provide binary counts. The different light pattern exposures and corresponding light pattern illuminations may be performed in an interleaved manner (e.g., first light pattern illumination and exposure, second light pattern illumination and exposure, first light pattern illumination and exposure, and so forth). Additional light pattern exposures under illumination according to additional light patterns may also be performed and interleaved (e.g., third light pattern illumination and exposure, fourth light pattern illumination and exposure, nth light pattern illumination and exposure). 
     The interleaved light pattern exposures are alternately performed (e.g., one after the other) over a frame capture time period, providing multiple subsets of binary counts. For example, a first subset of binary counts may be associated with the first light pattern exposure, a second temporally subsequent subset of binary counts may be associated with a second light pattern exposure, a third temporally subsequent subset of binary counts may be associated with first light pattern exposure, a fourth temporally subsequent subset of binary counts may be associated with second light pattern exposure, a fifth temporally subsequent subset of binary counts may be associated with first light pattern exposure, and so forth. As noted above, additional subsets of binary counts may correspond to additional light pattern illuminations and exposures and may be interleaved with the above. 
     Accordingly, subsets of binary counts associated with the same light pattern exposures (e.g., different subsets of binary counts associated with a first light pattern exposure) can be generated not temporally contiguous to one another. Furthermore, a subset of binary counts associated with one light pattern exposure (e.g., a second light pattern exposure) may temporally intervene between two subsets of binary counts associated with another light pattern exposure (e.g., a first light pattern exposure), and vice versa. 
     A system may then use all of the subsets of binary counts associated with each particular light pattern exposure (e.g., the first light pattern exposure, the second light pattern exposure, etc.) to generate a respective light pattern image for the particular light pattern exposure, even where the subsets of binary counts associated with the particular light pattern exposure are temporally noncontiguous. The different respective light pattern images may be used to generate pixel signatures for computing depth information. 
     The use of SPAD image sensors to capture light pattern images as described herein may provide a number of advantages over conventional systems and techniques for capturing light pattern images (e.g., for structured light depth computations). As noted above, conventional techniques for capturing light pattern images include utilizing CMOS or CCD sensors to fully expose and read out a first light pattern image before proceeding to fully expose and read out a subsequent light pattern image, which may give rise to motion artifacts and/or spatial misalignment between the different light pattern images. In contrast, by interleaving different light pattern exposures according to the present disclosure, the motion in the captured scene (and/or motion of the image sensor(s)) will affect all light pattern images in a similar manner, thereby mitigating motion artifacts and/or spatial misalignment between the light pattern images. Accordingly, the accuracy and/or usability of structured light depth imaging may be improved. 
     Having just described some of the various high-level features and benefits of the disclosed embodiments, attention will now be directed to  FIGS.  1  through  8   . These Figures illustrate various conceptual representations, architectures, methods, and supporting illustrations related to the disclosed embodiments. 
     Example Systems and Techniques for Structured Light Depth Computation Using SPADs 
       FIG.  1    illustrates various example components of a system  100  that may be used to implement one or more disclosed embodiments. For example,  FIG.  1    illustrates that a system  100  may include processor(s)  102 , storage  104 , sensor(s)  110 , SPAD array(s)  112 , input/output system(s)  114  (I/O system(s)  114 ), and communication system(s)  116 . Although  FIG.  1    illustrates a system  100  as including particular components, one will appreciate, in view of the present disclosure, that a system  100  may comprise any number of additional or alternative components. 
     The processor(s)  102  may comprise one or more sets of electronic circuitry that include any number of logic units, registers, and/or control units to facilitate the execution of computer-readable instructions (e.g., instructions that form a computer program). Such computer-readable instructions may be stored within storage  104 . The storage  104  may comprise physical system memory and may be volatile, non-volatile, or some combination thereof. Furthermore, storage  104  may comprise local storage, remote storage (e.g., accessible via communication system(s)  116  or otherwise), or some combination thereof. Additional details related to processors (e.g., processor(s)  102 ) and computer storage media (e.g., storage  104 ) will be provided hereinafter. 
     In some implementations, the processor(s)  102  may comprise or be configurable to execute any combination of software and/or hardware components that are operable to facilitate processing using machine learning models or other artificial intelligence-based structures/architectures. For example, processor(s)  102  may comprise and/or utilize hardware components or computer-executable instructions operable to carry out function blocks and/or processing layers configured in the form of, by way of non-limiting example, single-layer neural networks, feed forward neural networks, radial basis function networks, deep feed-forward networks, recurrent neural networks, long-short term memory (LSTM) networks, gated recurrent units, autoencoder neural networks, variational autoencoders, denoising autoencoders, sparse autoencoders, Markov chains, Hopfield neural networks, Boltzmann machine networks, restricted Boltzmann machine networks, deep belief networks, deep convolutional networks (or convolutional neural networks), deconvolutional neural networks, deep convolutional inverse graphics networks, generative adversarial networks, liquid state machines, extreme learning machines, echo state networks, deep residual networks, Kohonen networks, support vector machines, neural Turing machines, and/or others. 
     As will be described in more detail, the processor(s)  102  may be configured to execute instructions  106  stored within storage  104  to perform certain actions associated with imaging using SPAD arrays. The actions may rely at least in part on data  108  (e.g., avalanche event counting or tracking, etc.) stored on storage  104  in a volatile or non-volatile manner. 
     In some instances, the actions may rely at least in part on communication system(s)  116  for receiving data from remote system(s)  118 , which may include, for example, separate systems or computing devices, sensors, and/or others. The communications system(s)  118  may comprise any combination of software or hardware components that are operable to facilitate communication between on-system components/devices and/or with off-system components/devices. For example, the communications system(s)  118  may comprise ports, buses, or other physical connection apparatuses for communicating with other devices/components. Additionally, or alternatively, the communications system(s)  118  may comprise systems/components operable to communicate wirelessly with external systems and/or devices through any suitable communication channel(s), such as, by way of non-limiting example, Bluetooth, ultra-wideband, WLAN, infrared communication, and/or others. 
       FIG.  1    illustrates that a system  100  may comprise or be in communication with sensor(s)  110 . Sensor(s)  110  may comprise any device for capturing or measuring data representative of perceivable phenomenon. By way of non-limiting example, the sensor(s)  110  may comprise one or more image sensors, microphones, thermometers, barometers, magnetometers, accelerometers, gyroscopes, and/or others. 
       FIG.  1    also illustrates that the sensor(s)  110  may include SPAD array(s)  112 . As depicted in  FIG.  1   , a SPAD array  112  may comprise an arrangement of SPAD pixels  120  that are each configured to facilitate avalanche events in response to sensing a photon, as described hereinabove. SPAD array(s)  112  may be implemented on a system  100  (e.g., an MR HMD) to facilitate various functions such as image capture and/or computer vision tasks. 
     Furthermore,  FIG.  1    illustrates that a system  100  may comprise or be in communication with I/O system(s)  114 . I/O system(s)  114  may include any type of input or output device such as, by way of non-limiting example, a touch screen, a mouse, a keyboard, a controller, and/or others, without limitation. For example, the I/O system(s)  114  may include a display system that may comprise any number of display panels, optics, laser scanning display assemblies, and/or other components. 
       FIG.  1    conceptually represents that the components of the system  100  may comprise or utilize various types of devices, such as mobile electronic device  100 A (e.g., a smartphone), personal computing device  100 B (e.g., a laptop), a mixed-reality head-mounted display  100 C (HMD  100 C), an aerial vehicle  100 D (e.g., a drone), and/or other devices. Although the present description focuses, in at least some respects, on utilizing an HMD to implement techniques of the present disclosure, additional or alternative types of systems may be used. 
       FIG.  2    illustrates an example implementations of a single photon avalanche diode (SPAD) sensor in a head-mounted display (HMD). In particular,  FIG.  2    illustrates an example HMD  200 , which may correspond in at least some respects to the system  100  described hereinabove with reference to  FIG.  1   . In the example shown in  FIG.  2   , the HMD  200  includes SPAD sensor  202 . The SPAD sensor  202  may be configured to capture intensity image frames as described hereinabove (e.g., by counting photons or avalanche events detected over a frame capture time period on a per-pixel basis). Intensity images captured using the SPAD sensor  202  may be used for a variety of purposes, such as to facilitate image/video capture, pass-through imaging, depth computations (e.g., structured light depth imaging), object tracking, object segmentation, surface reconstruction, simultaneous localization and mapping (SLAM), and/or others. The HMD  200  may implement any number of other camera(s)  208  for the same, additional, or alternative purposes. 
       FIG.  2    also illustrates the HMD  200  as comprising an illuminator  204 , which may take the form of any suitable light emitting device. As will be described herein, a system (e.g., an HMD  200  or/or other device) may operate an illuminator (e.g., illuminator  204 ) in conjunction with one or more SPAD sensors (e.g., SPAD sensor  202 ) to capture images that are usable to facilitate structured light depth computation. For example, a system may selectively (or iteratively) activate the illuminator (e.g., in a pulsed manner) to allow the SPAD sensor(s)  202 A- 202 E to capture images of an environment as the environment is illuminated with different structured light patterns. The system may utilize the illuminator  204  as a “virtual camera” in conjunction with the SPAD sensor  202  to facilitate structured light depth computations. 
     In accordance with the present disclosure, the illuminator  204  may take on various forms to facilitate various types of illumination for capturing various types of illuminated images. For example, the illuminator  204  may be configured to emit visible light, infrared light, ultraviolet light, combinations thereof, and/or light in other spectral ranges. In some instances, as indicated above, the illuminator  204  may be configured to emit one or more structured light patterns, such as striped structured light patterns. Although the present disclosure focuses, in at least some respects, emitting striped structured light patterns with an illuminator to facilitate structured light depth computations, one will appreciate, in view of the present disclosure, that additional or alternative types of illumination may be used. For example, grayscale illumination, sinusoidal patterns, random patterns, and/or others may be used. 
     One will appreciate, in view of the present disclosure, that although  FIG.  2    only illustrates a single illuminator  204  on the HMD  200 , any number of illuminators may be used with any type(s) of system(s) to practice techniques of the present disclosure. 
       FIGS.  3 A- 3 E  illustrate an example of SPAD exposure operations performed over a frame capture time period. In particular,  FIG.  3 A  illustrates a SPAD array  300 , which may correspond to the SPAD array(s)  112  described hereinabove with reference to  FIG.  1   . In this regard, each SPAD pixel (e.g., SPAD pixel  302 ) of the SPAD array  300  is configurable to trigger avalanche events in response to detecting photons. The SPAD pixel  302  may be operated in a controlled, gated manner to facilitate different exposure operations for image acquisition. 
       FIG.  3 A  depicts ellipsis  304  between SPAD pixels of the SPAD array  300  and representations of exposure operations that will be described in more detail hereafter. The ellipsis  340  indicates that the exposure operations described hereafter may be performed using any number of SPAD pixels of a SPAD array  300  (e.g., all SPAD pixels). A single representation of the exposure operations is shown in  FIG.  3 A  (and subsequent Figures) for the sake of clarity and simplicity.  FIG.  3 A  additionally provides a time axis t to illustrate the temporal relationship among the different exposure operations that will be described hereinafter. 
       FIG.  3 A  shows that, to facilitate capturing of image frames usable for structured light depth computations, SPAD pixels of a SPAD array  300  are configured to perform multiple exposure operations in an interleaved manner. In particular,  FIG.  3 A  illustrates ambient exposure operations (labeled as “A” in  FIG.  3 A ), pattern 1 exposure operations (labeled as “P1” in  FIG.  3 A  and referred to hereinafter as “P1 exposure operations”), pattern 2 exposure operations (labeled as “P2” in  FIG.  3 A  and referred to hereinafter as “P2 exposure operations”), and pattern 3 exposure operations (labeled as “P3” in  FIG.  3 A  and referred to hereinafter as “P3 exposure operations”) performed over a frame capture time period  304 . In the example shown in  FIG.  3 A , the ambient exposure operations, P1 exposure operations, P2 exposure operations, and P3 exposure operations are performed in an interleaved manner, with sets of exposure operations including an ambient exposure operation, a P1 exposure operation, a P2 exposure operation, and a P3 exposure operation being performed one after another. 
     The ellipsis  390  indicates that any number of ambient exposure operations, P1 exposure operations, P2 exposure operations, and/or P3 exposure operations may be performed over the frame capture time period  304 . Furthermore, although the present example focuses, in at least some respects, on utilizing ambient exposure operations, P1 exposure operations, P2 exposure operations, and P3 exposure operations, ambient exposure operations may be omitted from the frame capture time period  304 , and/or additional or fewer pattern exposure operations may be interleaved within the frame capture time period  304 . 
       FIG.  3 A  furthermore illustrates (via dashed lines extending downward from the first ambient exposure operation) that an ambient exposure operation includes applying a set of shutter operations  306 . As noted above, applying a shutter operation to a SPAD pixel configures the SPAD pixel for photon detection by configuring the SPAD pixel to trigger avalanche events in response to detected photons. Thus, the presence of an avalanche event during a shutter operation indicates that the SPAD pixel detected a photon during the shutter operation. Whether a SPAD pixel experienced an avalanche event (and therefore detected a photon) during a shutter operation may be represented by a binary “1” or “0”, with “1” indicating that an avalanche event occurred during the shutter operation and with “0” indicating that no avalanche event occurred during the shutter operation. Accordingly,  FIG.  3 A  depicts binary counts  310  associated with each shutter operation  306 . The ellipsis  392  indicates that an ambient exposure operation may comprise any suitable number of shutter operations  306 . 
       FIG.  3 A  also depicts that the individual shutter operations  306  are performed over a particular gate time  308 . The gate time  308  is the duration over which a SPAD pixel becomes configured to trigger an avalanche event in response to a detected photon. 
     Although  FIG.  3 A  only explicitly depicts the set of shutter operations  306  associated with the first ambient exposure operation, other sets of shutter operations are applied to facilitate the other exposures (i.e., the P1, P2, and/or P3 exposure operations). For each P1 exposure operation, shutter operations  306  are performed while an illuminator (e.g., illuminator  204 ) is selectively activated (or pulsed) to project pattern 1 into the environment. As depicted in  FIG.  3 A , pattern 1 includes an illuminated vertical stripe (on the left, illustrated in white) and an unilluminated vertical stripe (on the right, illustrated in black). For each P2 exposure operation, shutter operations  306  are performed while an illuminator (e.g., illuminator  204 ) is selectively activated (or pulsed) to project pattern 2 into the environment. As depicted in  FIG.  3 A , pattern 2 includes two illuminated vertical stripes and two unilluminated vertical stripes, which are arranged in an alternating fashion. Furthermore, for each P3 exposure operation, shutter operations  306  are performed while an illuminator (e.g., illuminator  204 ) is selectively activated (or pulsed) to project pattern 3 into the environment. As depicted in  FIG.  3 A , pattern 3 includes four illuminated vertical stripes and four unilluminated vertical stripes, which are arranged in an alternating fashion. In contrast, for each ambient exposure operation, shutter operations  306  are performed while the illuminator(s) selectively refrain(s) from illuminating the environment. In this regard, over the frame capture time period, the illuminator(s) (e.g., including illuminator  204 ) alternately emits different light patterns and refrains from emitting light. 
     As noted above, the ellipsis  390  indicates that any number of ambient exposure operations and pattern exposure operations may be performed over the frame capture time period  304 . As will be described in more detail hereafter, the results of the ambient exposure operations may be combined to form an ambient image, and the results of the different pattern exposure operations may be combined to form different pattern images. To provide desirable images, in some instances, at least two pattern exposure operations for each light pattern used are performed over a frame capture time period  304 . Furthermore, in some instances, systems refrain from pausing performance of the shutter operations  306  within the frame capture time period  304  in order to avoid motion artifacts. For example, systems may refrain from pausing performance of the shutter operations to perform readout operations (although, under some configurations, readout operations may be performed passively during the frame capture time period  304  without pausing performance of the shutter operations). 
     The shutter operations  306  performed during each separate ambient exposure operation (i.e., without emission of light by the illuminator(s)) may provide a separate subset of binary counts. For example,  FIG.  3 B  illustrates various subsets  312 A,  312 B, and  312 C of binary counts. Each subset  312 A,  312 B, and  312 C of binary counts is associated with a different respective ambient exposure operation (illustrated in  FIG.  3 B  by the arrows extending from the various ambient exposure operations to the various subsets  312 A,  312 B, and  312 C of binary counts). As indicated above, and as depicted in  FIG.  3 B , the various subsets  312 A,  312 B, and  312 C of binary counts are not generated temporally contiguous to one another (i.e., because the pattern exposure operations intervene between the ambient exposure operations). The various subsets  312 A,  312 B, and  312 C of binary counts may be combined to form a set of binary counts  314 , and the set of binary counts  314  may be used to generate an ambient image, even though the various subsets  312 A,  312 B, and  312 C of binary counts that form the set of binary counts  314  are not temporally contiguous (see  FIG.  3 F ). 
     Similarly, the shutter operations  306  performed during each separate P1 exposure operation (i.e., during emission of pattern 1 by the illuminator(s)) may provide a separate subset of binary counts. For example,  FIG.  3 C  illustrates various subsets  316 A,  316 B, and  316 C of binary counts. Each subset  316 A,  316 B, and  316 C of binary counts is associated with a different respective P1 exposure operation (illustrated in  FIG.  3 C  by the arrows extending from the various P1 exposure operations to the various subsets  316 A,  316 B, and  316 C of binary counts). As indicated above, and as depicted in  FIG.  3 C , the various subsets  316 A,  316 B, and  316 C of binary counts are not generated temporally contiguous to one another (i.e., because ambient exposure operations, P2 exposure operations, and P3 exposure operations intervene between the P1 exposure operations). The various subsets  316 A,  316 B, and  316 C of binary counts may be combined to form a set of binary counts  318 , and the set of binary counts  318  may be used for generating a pattern 1 image, even though the various subsets  316 A,  316 B, and  316 C of binary counts that form the set of binary counts  318  are not temporally contiguous (see  FIG.  3 F ). 
     Also, the shutter operations  306  performed during each separate P2 exposure operation (i.e., during emission of pattern 2 by the illuminator(s)) may provide a separate subset of binary counts. For example,  FIG.  3 D  illustrates various subsets  320 A,  320 B, and  320 C of binary counts. Each subset  320 A,  320 B, and  320 C of binary counts is associated with a different respective P2 exposure operation (illustrated in  FIG.  3 D  by the arrows extending from the various P2 exposure operations to the various subsets  320 A,  320 B, and  320 C of binary counts). As indicated above, and as depicted in  FIG.  3 D , the various subsets  320 A,  3206 , and  320 C of binary counts are not generated temporally contiguous to one another (i.e., because ambient exposure operations, P1 exposure operations, and P3 exposure operations intervene between the P2 exposure operations). The various subsets  320 A,  3206 , and  320 C of binary counts may be combined to form a set of binary counts  322 , and the set of binary counts  322  may be used for generating a pattern 2 image, even though the various subsets  320 A,  3206 , and  320 C of binary counts that form the set of binary counts  322  are not temporally contiguous (see  FIG.  3 F ). 
     In addition, the shutter operations  306  performed during each separate P3 exposure operation (i.e., during emission of pattern 3 by the illuminator(s)) may provide a separate subset of binary counts. For example,  FIG.  3 E  illustrates various subsets  324 A,  324 B, and  324 C of binary counts. Each subset  324 A,  324 B, and  324 C of binary counts is associated with a different respective P3 exposure operation (illustrated in  FIG.  3 E  by the arrows extending from the various P3 exposure operations to the various subsets  324 A,  324 B, and  324 C of binary counts). As indicated above, and as depicted in  FIG.  3 E , the various subsets  324 A,  324 B, and  324 C of binary counts are not generated temporally contiguous to one another (i.e., because ambient exposure operations, P1 exposure operations, and P2 exposure operations intervene between the P3 exposure operations). The various subsets  324 A,  324 B, and  324 C of binary counts may be combined to form a set of binary counts  326 , and the set of binary counts  326  may be used for generating a pattern 3 image, even though the various subsets  324 A,  324 B, and  324 C of binary counts that form the set of binary counts  318  are not temporally contiguous (see  FIG.  3 F ). 
     Although  FIG.  3 B  only illustrates a single set of binary counts  314  obtained from shutter operations  306  performed by a single SPAD pixel  302  during ambient exposure operations over the frame capture time period  304 , separate sets of binary counts from ambient exposure operations may be generated for each SPAD pixel of the SPAD array  300 . Similarly, although  FIG.  3 C  only illustrates a single set of binary counts  318  obtained from shutter operations  306  performed by a single SPAD pixel  302  during P1 exposure operations over the frame capture time period  304 , separate sets of binary counts from P1 exposure operations may be generated for each SPAD pixel of the SPAD array  300 . Also, although  FIG.  3 D  only illustrates a single set of binary counts  322  obtained from shutter operations  306  performed by a single SPAD pixel  302  during P2 exposure operations over the frame capture time period  304 , separate sets of binary counts from P2 exposure operations may be generated for each SPAD pixel of the SPAD array  300 . In addition, although  FIG.  3 E  only illustrates a single set of binary counts  326  obtained from shutter operations  306  performed by a single SPAD pixel  302  during P3 exposure operations over the frame capture time period  304 , separate sets of binary counts from P3 exposure operations may be generated for each SPAD pixel of the SPAD array  300 . 
       FIG.  3 F  illustrates sets of ambient exposure binary counts  328 , which includes each set of binary counts generated by each SPAD pixel of the SPAD array  300  during ambient exposure operations (see  FIG.  3 B ). Each set of binary counts of the sets of ambient exposure binary counts  328  (e.g., including set of binary counts  314  from  FIG.  3 B ) includes a respective plurality of subsets of binary counts (e.g., subsets  312 A- 312 C for set of binary counts  314 ) generated by a SPAD pixel (e.g., SPAD pixel  302 ) during the ambient exposure operations over the frame capture time period  304 . 
       FIG.  3 F  also illustrates ambient readout  330  performed using the sets of ambient exposure binary counts  328 . For a SPAD array, a “readout” comprises determining or outputting a number of photons detected over a set of shutter operations on a per-pixel basis. Pixels capturing brighter portions of a captured environment will have counted a greater number of photons than pixels capturing darker portions of the captured environment. The per-pixel number of photons detected over the set of shutter operations may therefore be used to form an intensity image. 
     Accordingly, ambient readout  330  may comprise determining or outputting the number of photons represented by each set of binary counts of the sets of ambient exposure binary counts  328 , where each set corresponds to a different SPAD pixel. Per-pixel intensity values may be determined based on the number of photons from each set, and the per-pixel intensity values may be used to generate the ambient image  332 . 
     Similarly,  FIG.  3 F  illustrates sets of P1 exposure binary counts  334 , which includes each set of binary counts generated by each SPAD pixel of the SPAD array  300  during P1 exposure operations (see  FIG.  3 C ). Each set of binary counts of the sets of P1 exposure binary counts  334  (e.g., including set of binary counts  318  from  FIG.  3 C ) includes a respective plurality of subsets of binary counts (e.g., subsets  316 A- 316 C for set of binary counts  318 ) generated by a SPAD pixel (e.g., SPAD pixel  302 ) during the P1 exposure operations over the frame capture time period  304 . 
       FIG.  3 F  also illustrates P1 readout  336  performed using the sets of P1 exposure binary counts  334 . P1 readout  328  may comprise determining or outputting the number of photons represented by each set of binary counts of the sets of P1 exposure binary counts  334 , where each set corresponds to a different SPAD pixel. Per-pixel intensity values may be determined based on the number of photons from each set, and the per-pixel intensity values may be used to generate the pattern 1 image  338 . 
     Furthermore,  FIG.  3 F  illustrates sets of P2 exposure binary counts  340 , which includes each set of binary counts generated by each SPAD pixel of the SPAD array  300  during P2 exposure operations (see  FIG.  3 D ). Each set of binary counts of the sets of P2 exposure binary counts  340  (e.g., including set of binary counts  322  from  FIG.  3 D ) includes a respective plurality of subsets of binary counts (e.g., subsets  320 A- 320 C for set of binary counts  322 ) generated by a SPAD pixel (e.g., SPAD pixel  302 ) during the P2 exposure operations over the frame capture time period  304 . 
       FIG.  3 F  also illustrates P2 readout  342  performed using the sets of P2 exposure binary counts  340 . P2 readout  342  may comprise determining or outputting the number of photons represented by each set of binary counts of the sets of P2 exposure binary counts  340 , where each set corresponds to a different SPAD pixel. Per-pixel intensity values may be determined based on the number of photons from each set, and the per-pixel intensity values may be used to generate the pattern 2 image  344 . 
     Also,  FIG.  3 F  illustrates sets of P3 exposure binary counts  346 , which includes each set of binary counts generated by each SPAD pixel of the SPAD array  300  during P3 exposure operations (see  FIG.  3 E ). Each set of binary counts of the sets of P3 exposure binary counts  346  (e.g., including set of binary counts  326  from  FIG.  3 E ) includes a respective plurality of subsets of binary counts (e.g., subsets  324 A- 324 C for set of binary counts  326 ) generated by a SPAD pixel (e.g., SPAD pixel  302 ) during the P3 exposure operations over the frame capture time period  304 . 
       FIG.  3 F  also illustrates P3 readout  348  performed using the sets of P3 exposure binary counts  346 . P3 readout  348  may comprise determining or outputting the number of photons represented by each set of binary counts of the sets of P3 exposure binary counts  348 , where each set corresponds to a different SPAD pixel. Per-pixel intensity values may be determined based on the number of photons from each set, and the per-pixel intensity values may be used to generate the pattern 3 image  350 . 
       FIG.  3 F  illustrates ambient image  332  including a representation of a moving ball captured by the SPAD array  300 .  FIG.  3 F  also illustrates the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350  as each including a respective representation of the same moving ball captured by the SPAD array  300  under their respective illumination conditions. Because the ambient exposure operations used to generate the ambient image  332  are interleaved over the same frame capture time period  304  with the pattern illumination operations used to generate the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350 , the moving ball is depicted in the ambient image  324 , the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350  in a spatially aligned manner. Stated differently, the motion of the ball will affect the capturing of the ambient image  324 , the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350  in substantially the same way. 
     Because the representations of the moving ball are substantially spatially aligned in the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350 , these images may be well-suited for determining pixel signatures for facilitating depth calculations.  FIGS.  4 A- 4 C and  5 A- 5 B  illustrate examples of determining a pixel signature associated with a SPAD pixel. In particular,  FIG.  4 A  illustrates the ambient image  322 , the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350 .  FIG.  4 A  depicts an image pixel of the ambient image  332  corresponding to SPAD pixel  302  discussed hereinabove with reference to  FIGS.  3 A- 3 E  (labeled in  FIG.  4 A  as “ 302 ”). The following discussion describes how a pixel signature may be obtained for the SPAD pixel  302  (or the image pixel corresponding thereto). 
       FIG.  4 A  illustrates the set of binary counts  314  generated based on shutter operations  306  of the SPAD pixel  302  during ambient exposure operations (see as described hereinabove with reference to  FIG.  3 B ).  FIG.  4 A  furthermore illustrates an ambient light measure  402  determined based on the set of binary counts  314 . The ambient light measure  402  comprises a representation of number of detected photons represented in the set of binary counts  314 . In the present example, the set of binary counts  314  indicates that 6 photons were detected at the SPAD pixel  302  during the ambient exposure operations, providing an ambient light measure  402  of 6 photons (as illustrated in  FIG.  4 A ). 
     As is shown in  FIG.  4 A , the ambient light measure  402  may be used to determine a threshold number of photons  404 . The threshold number of photons  404  indicates, in some instances, a cutoff number of photons that is usable to determine whether, while capturing the different pattern images, the SPAD pixel  302  captured a portion of a scene that was illuminated by a structured light pattern. In some instances, the threshold number of photons  402  is greater than the ambient light measure  402 . 
     As will be described hereinbelow, the threshold number of photons  404  may be used to determine signature values for the SPAD pixel  302  associated with the capturing of each of the pattern images (i.e., the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350 ), and the pixel signature for the SPAD pixel  302  may be based on the signature values. 
       FIG.  4 B  illustrates the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350 , as well as a representation of the SPAD pixel  302  on each pattern image to visually depict the portion of the various pattern images captured by the SPAD pixel  302 .  FIG.  4 B  includes the set of binary counts  318  captured by the SPAD pixel  302  during the P1 exposure operations for generating the pattern 1 image  338 .  FIG.  4 B  furthermore illustrates a number of photons  406  determined based on the set of binary counts  318  (i.e., 15 photons) and shows a signature value  408  determined based on whether the number of photons  406  satisfies the threshold number of photons  404  discussed above. 
     As depicted in  FIG.  4 B , an arrow extends from the number of photons  406  to a decision block within the signature value  408 . In the example shown in  FIG.  4 B , if the number of photons  406  satisfies the threshold number of photons  404 , the signature value  408  is defined as a value of “1,” and if the number of photons  406  fails to satisfy the threshold number of photons  404 , the signature value  408  is defined as a value of “0.”  FIG.  4 B  illustrates an example in which, during the P1 exposure operations performed over the frame capture time period  304  to generate the pattern 1 image  338 , the SPAD pixel  302  captured a portion of the environment that was illuminated by pattern 1 as projected by an illuminator (see pattern 1 image  338  in  FIG.  4 B ). Accordingly,  FIG.  4 B  illustrates the number of photons  406  represented by the set of binary counts  318  as 15 photons, which far exceeds the ambient light measure  402  and therefore, in this example, satisfies the threshold number of photons  404 . Accordingly,  FIG.  4 B  shows the “Yes” and “1” elements within the signature value  408  in bold and underlined format, indicating that the number of photons  406  satisfies the threshold number of photons  404 , resulting in a signature value  408  of “1” for the pattern 1 portion of the SPAD pixel  302 . 
       FIG.  4 B  similarly shows the set of binary counts  322  captured by the SPAD pixel  302  during the P2 exposure operations for generating the pattern 2 image  344 .  FIG.  4 B  furthermore illustrates a number of photons  410  determined based on the set of binary counts  322  (i.e., 6 photons) and shows a signature value  412  determined based on whether the number of photons  410  satisfies the threshold number of photons  404  discussed above. As shown in  FIG.  4 B , the number of photons  410  represented by the set of binary counts  322  is 6 photons, which fails to exceed the ambient light measure  402  and therefore, in this example, fails to exceed the threshold number of photons  404 . Accordingly,  FIG.  4 B  shows the “No” and “0” elements within the signature value  412  in bold and underlined format, indicating that the number of photons  412  fails to satisfy the threshold number of photons  404 , resulting in a signature value  412  of “0” for the pattern 2 portion of the SPAD pixel  302 . 
     Furthermore,  FIG.  4 B  illustrates the set of binary counts  326  captured by the SPAD pixel  302  during the P3 exposure operations for generating the pattern 3 image  350 .  FIG.  4 B  furthermore illustrates a number of photons  414  determined based on the set of binary counts  326  (i.e., 15 photons) and shows a signature value  416  determined based on whether the number of photons  414  satisfies the threshold number of photons  404  discussed above. As shown in  FIG.  4 B , the number of photons  414  represented by the set of binary counts  326  is 15 photons, which far exceeds the ambient light measure  402  and therefore, in this example, satisfies the threshold number of photons  404 . Accordingly,  FIG.  4 B  shows the “Yes” and “1” elements within the signature value  416  in bold and underlined format, indicating that the number of photons  416  satisfies the threshold number of photons  404 , resulting in a signature value  416  of “1” for the pattern 3 portion of the SPAD pixel  302 . 
     Although the foregoing examples utilize measured numbers of photons to determine signature values, other metrics based on sets of binary counts may be used (e.g., average numbers of photons). 
     The signature values  408 ,  412 , and  416  may be combined to form a pixel signature associated with the SPAD pixel  302 . Because the signatures values  408 ,  412 , and  416  are determined based on the sets of binary counts  318 ,  322 , and  326 , respectively, the pixel signature is also based on the binary counts  318 ,  322 , and  326 .  FIG.  4 C  illustrates a pixel signature  418  formed from the signature values  408 ,  412 , and  416  by using a sequence of the signature values  408 ,  412 , and  416  as an identifier. One will appreciate, in view of the present disclosure, that a pixel signature may take on other forms and that the signature values (which may also take on other forms) may be combined and/or transformed in various ways to generate the pixel signature. 
     It will be appreciated, in view of the present disclosure, that pixel signatures may be determined for any number of SPAD pixels (or image pixels associated with the SPAD pixels).  FIGS.  4 A- 4 C  focus, in at least some respects, on techniques for generating pixel signatures that utilize per-pixel threshold numbers of photons determined based on the ambient image  332  (e.g., based on per-pixel ambient light measures  402 ). However, other techniques may be used to generate pixel signatures. 
     An additional technique for generating pixel signatures is discussed hereinbelow with reference to  FIGS.  5 A and  5 B .  FIG.  5 A  illustrates the ambient image  332 , the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350 . Rather than determining per-pixel threshold numbers of photons,  FIG.  5 A  illustrates performing ambient light subtraction  502  on the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350 . The ambient light subtraction  502  may comprise subtracting the intensity values, ambient light measures, or binary counts associated with the ambient image  332  from each of the pattern 1 image  338 , the pattern 2 image  344 , and the pattern 3 image  350  on a per-pixel basis. In the example shown in  FIG.  5 A , the ambient light subtraction operation  502  provides pattern 1 illumination image  504 , pattern 2 illumination image  506 , and pattern 3 illumination image  508 . 
     In some instances, by subtracting the ambient light from the pattern images to generate pattern illumination images, one or more common photon thresholds may be used to determine signature values for all SPAD pixels used to capture the images (e.g., a threshold of 1 photon). For example,  FIG.  5 B  illustrates image pixels in pattern 1 illumination image  504 , pattern 2 illumination image  506 , and pattern 3 illumination image  508  that correspond to the SPAD pixel  302 .  FIG.  5 B  also illustrates an illumination value  510  (i.e., 9 photons) for the SPAD pixel  302 , which is, in some instances, provided by subtracting an ambient light measure (e.g., ambient light measure  402 ) from a number of photons (e.g., number of photons  406 ) associated with the pattern 1 image  338 . In the example shown in  FIG.  5 B , the illumination value  510  for the SPAD pixel  302  is 9 photons, and the signature value  512  for the pattern 1 portion of the SPAD pixel  302  is determined to be a value of “1” based on the illumination value  510  satisfying a photon count threshold that is shared with other SPAD pixels (e.g., a threshold of 1 photon). 
       FIG.  5 B  also illustrates an illumination value  514  for the SPAD pixel  302 . In the example shown in  FIG.  5 B , the illumination value  514  for the SPAD pixel  302  is 0 photons, and the signature value  516  for the pattern 2 portion of the SPAD pixel  302  is determined to be a value of “0” based on the illumination value  514  failing to satisfy a photon count threshold that is shared with other SPAD pixels (e.g., a threshold of 1 photon). Furthermore,  FIG.  5 B  illustrates an illumination value  518  for the SPAD pixel  302 . In the example shown in  FIG.  5 B , the illumination value  518  for the SPAD pixel  302  is 9 photons, and the signature value  520  for the pattern 3 portion of the SPAD pixel  302  is determined to be a value of “1” based on the illumination value  518  satisfying a photon count threshold that is shared with other SPAD pixels (e.g., a threshold of 1 photon). Similar to pixel signature  418  discussed above, pixel signature  522  may be determined based on the signature values  512 ,  516 , and  520 . 
     The present examples focus on using three light patterns to generate pixel signatures. It will be appreciated, in view of the present disclosure, that any number of light patterns may be used to facilitate unique pixel signatures for pixels lying along the same scanline. 
     In some instances, SPAD pixels may be configured to perform any processing described herein on-sensor to provide sensor output that corresponds to pixel signatures, which may facilitate computationally efficient depth processing. 
     Furthermore, in some instances, parameters associated with capturing sets of binary counts for facilitating depth processing may be dynamically modified to account for the amount of ambient light present in a captured scene, the distance between image sensors and captured objects, and/or other factors. For example, where a captured scene includes high ambient light, additional light may need to be emitted by an illuminator to provide for sufficient differentiation between portions of the scene that are illuminated with pattern light and portions of the scene that are not. Accordingly, in some instances, based on detected ambient light (e.g., determined by capturing an ambient light image), a system may modify a pulse time period for pulsing an illuminator, a number of pulses associated with emitting one or more light patterns, an amount of light (e.g., intensity of light) emitted by an illuminator, a frame capture time period, shutter timing associated with shutter operations (e.g., gate time  308 ), and/or other parameters. 
     As noted hereinabove, an illuminator (e.g., illuminator  204 ) may be used as a “virtual camera” in conjunction with a SPAD sensor (e.g., SPAD sensor  202 ) to facilitate structured light depth calculations.  FIG.  6    illustrates pattern 1, pattern 2, and pattern 3, which were project into the captured environment to generate pixel signatures  418  and/or  522  according to examples of the present disclosure.  FIG.  6    also illustrates a virtual pixel  602  associated with particular pixel coordinates relative to pattern 1, pattern 2, and pattern 3. A system may determine whether the particular pixel coordinates correspond to an illuminated portion or an unilluminated portion for each structured light pattern (e.g., pattern 1, pattern 2, and pattern 3). 
     Accordingly,  FIG.  6    depicts the virtual pixel  602  overlaid on the various patterns (i.e., pattern 1, pattern 2, and pattern 3) and depicts an arrow extending from the virtual pixel  602  to a respective decision block associated with a respective illumination value (i.e., illumination value  604  for pattern 1, illumination value  606  for pattern 2, and illumination value  608  for pattern 3). The decision blocks associated with the illumination values  604 ,  606 , and  608  conceptually depict a determination of whether the coordinates of the virtual pixel  602  correspond to an illuminated or unilluminated portion of the various patterns. As is evident from  FIG.  6   , the pixel coordinates of the virtual pixel  602  correspond to an illuminated portion of pattern 1, which is represented in the example of  FIG.  6    by an illumination value of “1” for the illumination value  604  (with “1” and “Yes” illustrated in bold and underlined format).  FIG.  6    also depicts the pixel coordinates of the virtual pixel  602  corresponding to an unilluminated portion of pattern 2, which is represented in the example of  FIG.  6    by an illumination value of “0” for the illumination value  606 . Furthermore,  FIG.  6    illustrates the pixel coordinates of the virtual pixel  602  corresponding to an illuminated portion of pattern 2, which is represented in the example of  FIG.  6    by an illumination value of “1” for the illumination value  608 . 
       FIG.  6    illustrates a virtual pixel signature  610  generated based on the illumination values  604 - 608  discussed above. In the example of  FIG.  6   , the virtual pixel signature  610  is represented by a sequence of values including “1”, “0”, and “1”, which matches the sequence of values that define the pixel signatures  418  and  522  discussed above for the SPAD pixel  302 . 
     A system may determine a depth value for a SPAD pixel by identifying a corresponding virtual pixel (on a same scanline) that has a virtual pixel signature that matches the pixel signature for the SPAD pixel and using the pixel coordinates of the SPAD pixel and the corresponding virtual pixel to determine a disparity value (which may be used to calculate depth).  FIG.  7    conceptually depicts such functionality, illustrating pixel coordinates  702  associated with the SPAD pixel  302  and the pixel signature  522  being used with pixel coordinates  704  associated with the corresponding virtual pixel  602  and the matching virtual pixel signature  610  to determine a disparity value  706 . Such functionality may be performed for all SPAD pixels of a SPAD array to determine per-pixel disparity and/or depth values for generating a depth map. 
     Example Method(s) for Structured Light Depth Computation Using SPADs 
     The following discussion now refers to a number of methods and method acts that may be performed by the disclosed systems. Although the method acts are discussed in a certain order and illustrated in a flow chart as occurring in a particular order, no particular ordering is required unless specifically stated, or required because an act is dependent on another act being completed prior to the act being performed. One will appreciate that certain embodiments of the present disclosure may omit one or more of the acts described herein. 
       FIG.  8    illustrates an example flow diagram  800  depicting acts associated with structured light depth computation using SPADs. The discussion of the various acts represented in flow diagram  800  include references to various hardware components described in more detail with reference to  FIGS.  1  and/or  2   . 
     Act  802  of flow diagram  800  includes, over a frame capture time period, selectively activating an illuminator to perform interleaved structured light illumination operations, the interleaved structured light illumination operations comprising alternately emitting at least a first structured light pattern from the illuminator and emitting at least a second structured light pattern from the illuminator. Act  802  is performed, in some instances, utilizing one or more components of a system  100  (e.g., processor(s)  102 , storage  104 , sensor(s)  110 , SPAD array(s)  112 ), I/O system(s)  114 , communication system(s)  116 ), an illuminator (e.g., illuminator  204 ), and/or other components. In some instances, the first structured light pattern and the second structured light pattern comprise striped structured light patterns. Furthermore, in some instances the second structured light pattern comprises more stripes than the first structured light pattern. Furthermore, in some instances, performing the interleaved structured light illumination operations comprises causing the first structured light pattern and the second structured light pattern to be emitted from the illuminator at least twice over the frame capture time period. Still furthermore, in some instances, performing the plurality of sequential shutter operations comprises refraining from pausing performance of the shutter operations to perform a readout operation during the frame capture time period. 
     Act  804  of flow diagram  800  includes, over the frame capture time period, performing a plurality of sequential shutter operations to configure each SPAD pixel of a SPAD array to enable photon detection, the plurality of sequential shutter operations generating, for each SPAD pixel of the SPAD array, a plurality of binary counts indicating whether a photon was detected during each of the plurality of sequential shutter operations. Act  804  is performed, in some instances, utilizing one or more components of a system  100  (e.g., processor(s)  102 , storage  104 , sensor(s)  110 , SPAD array(s)  112 ), I/O system(s)  114 , communication system(s)  116 ) and/or other components. 
     Act  806  of flow diagram  800  includes, for each SPAD pixel of the SPAD array, determining a respective pixel signature based on at least (i) a first set of binary counts generated via the SPAD pixel during illumination by the first structured light pattern over the frame capture time period and (ii) a second set of binary counts generated via the SPAD pixel during illumination by the second structured light pattern over the frame capture time period. Act  806  is performed, in some instances, utilizing one or more components of a system  100  (e.g., processor(s)  102 , storage  104 , sensor(s)  110 , SPAD array(s)  112 ), I/O system(s)  114 , communication system(s)  116 ) and/or other components. In some instances, the first set of binary counts comprises a first plurality of subsets of binary counts generated via the SPAD pixel during illumination by the first structured light pattern over the frame capture time period, and each of the first plurality of subsets of binary counts are not generated temporally contiguous to one another. In some instances, the second set of binary counts comprises a second plurality of subsets of binary counts generated via the SPAD pixel during illumination by the second structured light pattern over the frame capture time period, and each of the second plurality of subsets of binary counts are not generated temporally contiguous to one another. Furthermore, in some instances, at least one subset of binary counts of the second plurality of subsets of binary counts temporally intervenes between at least two subsets of binary counts of the first plurality of subsets of binary counts. 
     In some instances, for each SPAD pixel of the SPAD array, the respective pixel signature is based on at least a respective first signature value and a respective second signature value. In some implementations, the respective first signature value is determined by determining a first illumination value by subtracting a respective ambient light measure from a number of photons represented by the first set of binary counts and determining whether the first illumination value satisfies a threshold number of photons. Furthermore, in some instances, the respective second value is determined by determining a second illumination value by subtracting the respective ambient light measure from a number of photons represented by the second set of binary counts and determining whether the second illumination value satisfies the threshold number of photons. 
     Still furthermore, in some instances, for each SPAD pixel of the SPAD array, the respective pixel signature is based on at least a respective first signature value and a respective second signature value, the respective first signature value is based on whether a number of photons represented by the first set of binary counts satisfies a respective threshold number of photons, and the respective second signature value being based on whether a number of photons represented by the second set of binary counts. In some implementations, for each SPAD pixel of the SPAD array, the threshold number of photons is determined based on a respective ambient light measure. The respective ambient light measure is determined based on an ambient light image frame captured using the SPAD array while refraining from emitting a structured light pattern from the illuminator. In some instances, the ambient light image frame is generated based on photons detected by SPAD pixels of the SPAD array during the frame capture time period while refraining from emitting a structured light pattern from the illuminator. 
     Act  808  of flow diagram  800  includes determining a plurality of virtual pixel signatures using at least the first structured light pattern and the second structured light pattern. Act  808  is performed, in some instances, utilizing one or more components of a system  100  (e.g., processor(s)  102 , storage  104 , sensor(s)  110 , SPAD array(s)  112 ), I/O system(s)  114 , communication system(s)  116 ) and/or other components. In some instances, each virtual pixel signature of the plurality of virtual pixel signatures is associated with respective pixel coordinates relative to the first structured light pattern and the second structured light pattern. Each particular virtual pixel signature of the plurality of virtual pixel signatures may be based on (i) whether the respective pixel coordinates for the particular virtual pixel signature are associated with an illuminated portion or an unilluminated portion of the first structured light pattern and (ii) whether the respective pixel coordinates for the particular virtual pixel signature are associated with an illuminated portion or an unilluminated portion of the second structured light pattern. 
     Act  810  of flow diagram  800  includes generating a depth map by identifying correspondences between (i) the respective pixel signatures of the SPAD pixels of the SPAD array and (ii) the plurality of virtual pixel signatures. Act  810  is performed, in some instances, utilizing one or more components of a system  100  (e.g., processor(s)  102 , storage  104 , sensor(s)  110 , SPAD array(s)  112 ), I/O system(s)  114 , communication system(s)  116 ) and/or other components. 
     Act  812  of flow diagram  800  includes, based on an ambient light image frame captured using the SPAD array while refraining from emitting a structured light pattern from the illuminator, dynamically modifying (i) a pulse time period associated with emitting the first structured light pattern or the second structured light pattern, (ii) a number of pulses associated with emitting the first structured light pattern or the second structured light pattern over the frame capture time period, (iii) an amount of emitted light associated with emitting the first structured light pattern or the second structured light pattern, (iv) the frame capture time period, or (v) a shutter timing associated with performing the plurality of sequential shutter operations. Act  814  is performed, in some instances, utilizing one or more components of a system  100  (e.g., processor(s)  102 , storage  104 , sensor(s)  110 , SPAD array(s)  112 ), I/O system(s)  114 , communication system(s)  116 ) and/or other components. 
     Disclosed embodiments may comprise or utilize a special purpose or general-purpose computer including computer hardware, as discussed in greater detail below. Disclosed embodiments also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. Such computer-readable media can be any available media that can be accessed by a general-purpose or special-purpose computer system. Computer-readable media that store computer-executable instructions in the form of data are one or more “physical computer storage media” or “hardware storage device(s).” Computer-readable media that merely carry computer-executable instructions without storing the computer-executable instructions are “transmission media.” Thus, by way of example and not limitation, the current embodiments can comprise at least two distinctly different kinds of computer-readable media: computer storage media and transmission media. 
     Computer storage media (aka “hardware storage device”) are computer-readable hardware storage devices, such as RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSD”) that are based on RAM, Flash memory, phase-change memory (“PCM”), or other types of memory, or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code means in hardware in the form of computer-executable instructions, data, or data structures and that can be accessed by a general-purpose or special-purpose computer. 
     A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmission media can include a network and/or data links which can be used to carry program code in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above are also included within the scope of computer-readable media. 
     Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission computer-readable media to physical computer-readable storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer-readable physical storage media at a computer system. Thus, computer-readable physical storage media can be included in computer system components that also (or even primarily) utilize transmission media. 
     Computer-executable instructions comprise, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. The computer-executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims. 
     Disclosed embodiments may comprise or utilize cloud computing. A cloud model can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, etc.), service models (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.). 
     Those skilled in the art will appreciate that the invention may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, pagers, routers, switches, wearable devices, and the like. The invention may also be practiced in distributed system environments where multiple computer systems (e.g., local and remote systems), which are linked through a network (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links), perform tasks. In a distributed system environment, program modules may be located in local and/or remote memory storage devices. 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), central processing units (CPUs), graphics processing units (GPUs), and/or others. 
     As used herein, the terms “executable module,” “executable component,” “component,” “module,” or “engine” can refer to hardware processing units or to software objects, routines, or methods that may be executed on one or more computer systems. The different components, modules, engines, and services described herein may be implemented as objects or processors that execute on one or more computer systems (e.g., as separate threads). 
     One will also appreciate how any feature or operation disclosed herein may be combined with any one or combination of the other features and operations disclosed herein. Additionally, the content or feature in any one of the figures may be combined or used in connection with any content or feature used in any of the other figures. In this regard, the content disclosed in any one figure is not mutually exclusive and instead may be combinable with the content from any of the other figures. 
     The present invention may be embodied in other specific forms without departing from its spirit or characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.