Abstract:
A system and method for acquiring geometic information from images includes a modulated light source configured to provide light energy at a rate unperceivable by the human eye. A camera is configured to acquire images at a rate at which a differential pair of images is obtained such that one of the pair of images includes light from the light source and the other image of the pair does not include light from the light source. A comparison module is configured to compare the differential pair of images to create a depth map for three-dimension model creation.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention generally relates to digital graphics and, more particularly, to systems and methods for acquiring three-dimensional models of images. 
       BACKGROUND OF THE INVENTION 
       [0002]    Three-dimensional (3D) model acquisition, also known as 3D geometry recovery, 3D shape reconstruction, etc, is a challenging problem involving the recovery of 3D geometry (often represented as polynomial meshes) of objects and scenes from images taken by, for example, image sensors. There has been substantial work on the recovery of depth information or three-dimensional (3D) geometry of objects. One technique belongs to a category of approaches known as structured light methods, which recover the 3D geometry of an object by projecting specially designed light patterns onto the surface of an object and estimating the depth (i.e., the distance between the point on the surface to the camera) by analyzing the deformations of the projected patterns. 
         [0003]    Among many techniques for 3D geometry recovery, structured light approaches are highly accurate and can be implemented in real-time, making them promising for real-world applications. Nevertheless, current structured light approaches have several limitations that hinder their application to real-world scenarios. For example, to realize accurate acquisition, these techniques require high power light and high contrast patterns to be projected onto the object surfaces. This is often unacceptable to human subjects such as actors in a movie scene. 
         [0004]    In addition, current structured light techniques usually entail assuming that there is little or no texture on the object surface. The presence of the object texture would affect the appearance of the structured light patterns. Therefore, textures can significantly degrade the accuracy. Even further, when the captured images/videos are also used for content creation, such as making movies, the presence of structured light in the images and videos is often undesirable. 
         [0005]    The various 3D acquisition techniques can be classified as active and passive approaches. Passive approaches acquire 3D geometry from images or videos taken under regular lighting conditions. 3D geometry is computed using the geometric or photometric features extracted from images and videos. Two of the most widely used approaches are stereo algorithm and photometric techniques. Stereo algorithm techniques estimate depth based on the disparity of a stereo image pair that is taken by two cameras with different viewing angles. Disparity is estimated by comparing the pixels in the stereo image pair and computing the coordinate difference. Photometric techniques estimate the normal of a surface based on the shading effect caused by surface orientations. For real-world images, both methods are inaccurate and require high computational cost. Stereo algorithms fail to estimate the depths of the pixels on flat regions (regions without texture). The photometric technique requires the materials (i.e. BRDF, Bidirectional Reflectance Distribution Function) of the object surfaces to be known a priori. 
         [0006]    In contrast, active approaches are able to achieve high accuracy and real-time speed. Laser scanning, for example, is a technique that includes projecting laser light onto a surface and estimating the depth based on the time-of-flight principle or the deformation of the light pattern. The application of laser scanning is very restricted, however. 
         [0007]    Another technique referred to as structured light is preferred in the art. Structured light techniques project visible or invisible (infrared) patterns onto the surfaces of objects and estimate the depth according to the deformation of the patterns. Structured light approaches have been extensively studied in the computer vision community. The state-of-the-art techniques include composite structured light, which uses multiple stripes instead of single stripe and color structured light,-and implements color to distinguish and identify the stripes. However, to achieve high accuracy of depth estimation, these approaches require high strength structured light and/or smoothness of a surface such that the surface texture would not interfere with the structured light patterns. As such, recovering 3D geometry of real-world objects using currently available techniques is intrusive and inaccurate. 
       SUMMARY OF THE INVENTION 
       [0008]    The concepts of the present invention address the deficiencies of the prior art by providing a method and system for the acquisition of three-dimensional geometric information of real-world objects and images with high accuracy and real-time speed. 
         [0009]    In one embodiment of the present invention, a method for acquiring geometric information of images includes illuminating an object with a structured pattern at a modulated rate unperceivable by the human eye, capturing images of the object at a rate at which a differential pair of images is obtained such that a first image of the pair of images is illuminated and a second image of the pair of images is not illuminated, and comparing the differential pair of images to determine geometric information for the object. The method can further include implementing a frame selection process to recover a sequence of images that were not illuminated for the reproduction and display of a scene. 
         [0010]    In an alternate embodiment of the present invention, a system for acquiring geometric information of images includes a modulated light source configured to provide light energy at a rate unperceivable by the human eye for illuminating an image object, a camera configured to capture images of the object at a rate at which a differential pair of images is obtained such that a first image of the pair of images is illuminated and a second image of the pair of images is not illuminated, and a comparison module configured to compare the differential pair of images to determine geometric information for the object. The comparison module of the system can include a depth estimator configured to estimate depths of an object according to deformations of determined structured patterns of said modulated light, a comparison module configured to determine a respective three dimensional mesh from the depth map for determining a three dimensional model of the object and a frame selection module configured to eliminate frames illuminated by the modulated light to provide a sequence of images not illuminated by the modulated light for use in reproduction or display. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    The teachings of the present invention can be readily understood by considering the following detailed description in conjunction with the accompanying drawings, in which: 
           [0012]      FIG. 1  depicts a high level block diagram of a system for acquiring geometric information from images in accordance with an embodiment of the present invention; 
           [0013]      FIG. 2  depicts a diagram of a method for acquiring geometric information of images in accordance with one embodiment of the present invention; 
           [0014]      FIG. 3  depicts a diagram of a difference analysis process for producing a depth map in accordance with an illustrative embodiment of the present invention; 
           [0015]      FIG. 4  depicts a flow diagram of a method for acquiring geometric information of images in accordance with an embodiment of the present invention; and 
           [0016]      FIG. 5  depicts a diagram of a frame selection process implemented to produce an image sequence or video having no illuminated images in accordance with an embodiment of the present invention. 
       
    
    
       [0017]    It should be understood that the drawings are for purposes of illustrating the concepts of the invention and are not necessarily the only possible configuration for illustrating the invention. To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. 
       DETAILED DESCRIPTION OF THE INVENTION 
       [0018]    The present invention addresses the deficiencies of the prior art by providing a method and system for acquiring three-dimensional models from images. Although the present invention will be described primarily within the context of a video recording system, the specific embodiments of the present invention should not be treated as limiting the scope of the invention. It will be appreciated by those skilled in the art and informed by the teachings of the present invention that the concepts of the present invention can be advantageously applied in substantially any digital imaging system, which is capable of being used to acquire geometrical information, and/or any recording system including recording data by telephone, digital camera, movie camera and the like. That is, the following description illustrates the principles of the present invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. 
         [0019]    The elements depicted in the Figures can be implemented in various forms of hardware, software or combinations thereof. Preferably, these elements are implemented in a combination of hardware and software on one or more appropriately programmed general-purpose devices, which can include a processor, memory and input/output interfaces. Thus, for example, it will be appreciated by those skilled in the art that the block diagrams presented herein represent conceptual views of illustrative circuitry embodying the principles of the invention. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo-code, and the like represent various processes which may be substantially represented in computer readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. 
         [0020]      FIG. 1  depicts a high level block diagram of a system for acquiring geometric information from images in accordance with an embodiment of the present invention. The system  100  of  FIG. 1  illustratively comprises a computer  105 , a light source  104 , a camera  108  and an image or scene  103  to be recorded. In the system  100  of  FIG. 1 , the computer illustratively comprises a light control module  102 , which stores various programs for controlling the light source  104  and a comparison module  110 . The comparison module  110  of the computer  105  illustratively comprises a depth estimator  112 , a conversion module  114  and a frame selection module  116 . 
         [0021]    In the system  100  of  FIG. 1 , a flickering or modulated light source  104  projects structured patterns of light onto an object  113  in the scene  103 . A camera  108  is provided with a relatively high frame rate that captures the scene illuminated by the structured light. In one embodiment of the present invention, the frame rate of the camera is equal to or greater than the flickering rate of the structured light. The light source  104  of  FIG. 1  is connected to the PC  105 , which controls the generation of the structured light patterns and simulates the flickering effects. Structured patterns can be generated by, for example, the stored programs  107  in the light control module  102 . That is, the flickering effects can be generated by switching on and off the light pattern in accordance with details contained in the program  107 . 
         [0022]    To minimize the light disturbance caused by the light source on the objects, in one embodiment of the present invention, the system  100  of  FIG. 1  utilizes a flickering frequency for the light source  104  that is high enough so that the human vision system is unable to perceive it (e.g., 60 Hz or greater). That is, the light source provides structured light invisible to human eyes based on the fact that the sensitivity of human eyes is substantially reduced when the frequency of a flickering light source exceeds a certain threshold. As such, when objects are captured using the camera  108  the light is invisible and the disturbance is minimal if any. With the relatively high frame rate, images are captured as differential image pairs. The differential image pairs comprise two consecutive images in the captured image sequence in which one is lit by the structured light and the other is not. By analyzing differences between the images in the differential image pair, the textures on the object surface can be eliminated, while the shape and color of the structured light patterns are preserved. 
         [0023]    For example, in one embodiment of the present invention, the comparison module  110  of the computer  105  analyzes the differences between the images in the differential image pairs and eliminates the non-illuminated portions of the object in a scene. That is, in one embodiment of the present invention, a difference analysis includes comparing an image including the structured light patterns to the image not having the structured light pattern and determining any differences. 
         [0024]      FIG. 2  depicts a diagram of an example application of a method for acquiring geometric information of images in accordance with one embodiment of the present invention. In  FIG. 2 , structured light patterns  126 , in accordance with embodiments of the present invention, are acquired by the camera  108  in a first image  128  of a differential image pair  120 . The second image  130  of each pair  120  captured by the camera  108  does not include the structured light pattern. 
         [0025]    For example, in one embodiment of the present invention, color structured light projects color stripes onto a scene to be used for estimating the depth of an illuminated object. More specifically, defining the color vector at a point on the surface seen by the camera  108  as S=[s r ,s g ,s b ] T , the projector crosstalk matrix can be characterized according to equation one (1), which follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     X 
                     = 
                     
                       [ 
                       
                         
                           
                             
                               x 
                               11 
                             
                           
                           
                             
                               x 
                               12 
                             
                           
                           
                             
                               x 
                               13 
                             
                           
                         
                         
                           
                             
                               x 
                               21 
                             
                           
                           
                             
                               x 
                               22 
                             
                           
                           
                             
                               x 
                               23 
                             
                           
                         
                         
                           
                             
                               x 
                               31 
                             
                           
                           
                             
                               x 
                               32 
                             
                           
                           
                             
                               x 
                               33 
                             
                           
                         
                       
                       ] 
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
         [0000]    and the scene albedo matrix can be characterized according to equation two (2), which follows: 
         [0000]    
       
         
           
             
               
                 
                   F 
                   = 
                   
                     
                       [ 
                       
                         
                           
                             
                               ρ 
                               11 
                             
                           
                           
                             0 
                           
                           
                             0 
                           
                         
                         
                           
                             0 
                           
                           
                             
                               ρ 
                               22 
                             
                           
                           
                             0 
                           
                         
                         
                           
                             0 
                           
                           
                             0 
                           
                           
                             
                               ρ 
                               33 
                             
                           
                         
                       
                       ] 
                     
                     . 
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Furthermore, the projector illumination vector can be characterized as P=[p r  cos θ,p g  cos θ, p b  cos θ] T , where θ is the angle between the surface normal and the projector incidence light, the environment lighting vector can be characterized as E=[e r ,e g ,e b ] T  and an ambient light vector be characterized as O=[o r ,o g ,o b ] T . The above quantities can be related according to equation three (3), which follows: 
         [0000]        S=XF ( P+E )+ O.    (3) 
         [0000]    The structured light color p r ,p g ,p b  in the illumination vector P is determined given the color vector S observed by the camera. Once p r ,p g ,p b  is determined, the values are matched to the structured light patterns and are used to estimate the depth of a relative point on the surface of an object. By subtracting the two frames in the differential image pairs, the effects of the environmental lighting and ambient light can be characterized according to equation four (4), which follows: 
         [0000]      δ t   S=S ( t+ 1)− S ( t )= XF ( P+E )+ O−[XFE+O]=XFP,    (4) 
         [0000]    where S(t) and S(t+1) are the frames captured by the camera in the time t and t+1. By estimating the cross talk matrix X, the camera-projector rectification stage, FP, can be determined by multiplying δ t S with X −1 . However, the scene albedo vector F and surface orientation terms in the illumination vector still need to be eliminated. To do this, a spatial difference based method is employed to factor out the albedo and surface orientation. In one embodiment of the present invention, the scene albedo vector, FP, can be characterized according to equation five (5), which follows: 
         [0000]        FP =└ρ r  cos θ p   r ,ρ g  cos θ p   g ,ρ b  cos θ p   b ┘.   (5) 
         [0026]    It is assumed that the albedo of neighboring pixels is substantially the same as described above because, typically, an albedo map is piecewise constant and the surface is smooth. As such, in equation (5) above, θ is close to constant within a local region. It is further assumed that a pixel  1  and a pixel  2  are neighborhood pixels. As such, excluding the scene albedo and surface orientation terms, equation (5) can be rewritten and characterized according to equation six (6) which follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     δ 
                      
                     
                         
                     
                      
                     log 
                      
                     
                         
                     
                      
                     FP 
                   
                   = 
                   
                     
                       
                         log 
                          
                         
                             
                         
                          
                         
                           FP 
                           1 
                         
                       
                       - 
                       
                         log 
                          
                         
                             
                         
                          
                         
                           FP 
                           2 
                         
                       
                     
                     = 
                     
                       
                         [ 
                         
                           
                             log 
                              
                             
                               
                                 p 
                                 1 
                                 r 
                               
                               
                                 p 
                                 2 
                                 r 
                               
                             
                           
                           , 
                           
                             log 
                              
                             
                               
                                 p 
                                 1 
                                 g 
                               
                               
                                 p 
                                 2 
                                 g 
                               
                             
                           
                           , 
                           
                             log 
                              
                             
                               
                                 p 
                                 1 
                                 b 
                               
                               
                                 p 
                                 2 
                                 b 
                               
                             
                           
                         
                         ] 
                       
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Therefore, if the log difference δ log FP is utilized to calculate correspondence and perform depth estimation, the scene albedo and local surface orientation are considered invariant to such determinations. Although the depth estimation example is described above as implementing a color structure light, it should be appreciated by those skilled in the art and informed by the teachings herein that depth calculations can be realized using techniques for depth estimation from composite structured light or color structured light methods and the like. 
         [0027]    For example and referring back to  FIG. 1 , the comparison module  110  includes a depth estimator  112 , which estimates the depth according to the deformation of the structured patterns using, in one embodiment, the log difference, δ log FP, as characterized in equation (6) above. The depth estimator  112  outputs a depth map of the scene and objects in it. 
         [0028]      FIG. 3  depicts a diagram of a difference analysis process for producing a depth map in accordance with an illustrative embodiment of the present invention. In the example of  FIG. 4 , a difference analysis is applied to a differential image pair  120  to separate portions of an object image  130  from an illuminated object image  128  to determine a structured pattern  134 . The structured pattern  134  is then used by, for example, the depth estimator  112  of  FIG. 1  to determine a depth map  136 . 
         [0029]    Subsequently, the depth information (e.g., the depth map) is used to determine a three-dimensional (3D) model for the illuminated object. More specifically and referring back to the embodiment of  FIG. 1 , the comparison module  110  includes a conversion module  114 . In one embodiment of the present invention, the conversion module  114  converts the depth map into 3D meshes and combines multiple meshes taken from different viewpoints into one 3D mesh to produce a 3D model for the illuminated object. There are several existing well-known techniques in the art to convert a range or depth map into a 3D mesh and as such the details of such techniques will not be described in detail herein. For example, in one embodiment, the conversion of the depth map into 3D meshes can be realized by capturing the range images from different angles and then aligning and stitching them together to create a full 3D mesh/model. 
         [0030]    Subsequently, image sequences or videos without structured light patterns can be fully recovered in the post-processing stage. More specifically, a frame selection process  116  can be performed by, for example, the frame selection module  116  of  FIG. 1  to separate the frames not illuminated by the structure light from the frames illuminated by the structured light to produce an image sequence that does not include images illuminated by the structured light such that reproduction and display of a scene is not negatively affected. Such a process is described in greater detail below. 
         [0031]      FIG. 4  depicts a flow diagram of a method for acquiring geometric information of images in accordance with an embodiment of the present invention. The method  400  begins at step  402  in which an object is illuminated with flickering light as described above. In one embodiment, the flickering light is invisible to human eyes and can comprise a frequency of 60 Hz, although other frequencies can be used. As such, high accuracy and real-time speed can be achieved even for objects with strong textures. The method  400  then proceeds to step  404 . 
         [0032]    At step  404 , the images produced as the flickering light illuminates the reference object are captured by a camera as described above. The method  400  then proceeds to step  406 . 
         [0033]    At step  406 , a differential analysis is applied to a produced differential image pair (e.g., a pair of images produced in which a first image is lit by the flickering structured light and a second is not) to identify and isolate the structured light pattern produced on the illuminated object. The method  400  then proceeds to step  408 . 
         [0034]    At step  408 , a depth map is generated from a result of the differential image analysis of step  406 . The method then proceeds to step  410 . 
         [0035]    At step  410 ,  3 D models of the objects are created from the mesh object of the depth map. The method  400  is then exited. 
         [0036]    The method  400  of the present invention can further include step  412 . That is, at step  412 , a frame selection process is employed to discard the image frames illuminated by the flickering structured light, such that the image sequence of frames that were not illuminated can be recovered. 
         [0037]    More specifically, a frame selection process  116  can be performed by, for example, the frame selection module  116  of  FIG. 1  to separate the frames not illuminated by the structure light from the frames illuminated by the structured light to produce an image sequence that does not include images illuminated by the structured light such that reproduction and display of a scene is not negatively affected or corrupted by illumination. 
         [0038]    For example,  FIG. 5  depicts a diagram of a frame selection process implemented to produce an image sequence having no illuminated images in accordance with an embodiment of the present invention. The selection process of  FIG. 5  identifies frames having structured light patterns. For example, in one embodiment of the present invention, the identification of illuminated images can be realized by matching the patterns recovered from the difference analysis performed by the difference estimator  112 , as described above, to image frames to identify the image frames having the structured light pattern. After the frames with the structured light patterns are identified, these frames can be excluded from an original image sequence to generate an image sequence that does not include any illuminated images. Referring to  FIG. 5 , frames with structured light  128  are removed or discarded and the frames  130  are selected by the frame selection process. As such, a recovered sequence  140  includes frames that are needed for the reproduction or display of video, motion picture, still images and the like. 
         [0039]    Having described preferred embodiments for the acquisition of three-dimensional geometric information of real-world objects and scenes (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments of the invention disclosed which are within the scope and spirit of the invention as outlined by the appended claims. While the forgoing is directed to various embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. As such, the appropriate scope of the invention is to be determined according to the claims, which follow.