Abstract:
An apparatus and method for generating automated multi-view three dimensional pose and geometry estimation for the insertion of realistic and authentic views of synthetic objects into a real scene. A multi-view three dimensional estimation routine comprising the steps of feature tracking, pairwise camera pose estimation, computing camera pose for overlapping sequences and performing a global block adjustment to provide camera pose and scene geometric information for each frame of a scene. A match move routine may be used to insert a synthetic object into one frame of a video sequence based on the pose and geometric information of the frame, and calculate all other required object views of the synthetic object for the remaining frames using the pose and geometric information acquired as a result of the multi-view three dimensional estimation routine.

Description:
The invention relates to an image processing apparatus and, more particularly, the invention relates to method and apparatus for performing three dimensional scene estimation of camera pose and scene geometry, and providing a method for the authentic insertion of a synthetic object into a real scene using the information provided by the scene estimation routine. 
     BACKGROUND OF THE DISCLOSURE 
     Seamless three dimensional insertion of synthetic objects into real scene images requires tools to allow a user to situate synthetic objects with respect to real surfaces within a scene. To facilitate the creation of a realistic image, the synthetic objects need to be projected from all the given camera viewpoints of the real scene. The current methodology for inserting a synthetic object into a real scene includes the cumbersome task of tracking and recording the camera pose and calibration for each frame of a sequence. Thus the geometry and orientation of the synthetic object to be inserted can be matched to the camera pose and calibration data for each individual frame. This process of matching geometry and orientation of the synthetic image to the individual frame pose and calibration data is repeated frame to frame in order to maintain the realistic view of the inserted object through a sequence of frames. In current practice, the pose estimation is accomplished by modeling the three dimensional background scene prior to the pose computation. This is a tedious process. 
     In order to automate the insertion process, it is required that object insertion be performed in as few frames as possible, preferably one, and all the other views of the object be created automatically. For placement of the object with respect to the real scene, accurate albeit limited three dimensional geometry is required, for instance, estimation of local surface patches may suffice. For stable three dimensional appearance change of the object from the given camera positions, a reliable three dimensional camera pose computation is required. Furthermore, since the graphics objects are typically created using Euclidean geometry, it is strongly desirable that the real scene and the camera pose associated with the real scene be represented using Euclidean coordinates. Stability of the pose computation over extended image sequences is required to avoid jitter and drift in the location and appearance of synthetic objects with respect to the real scene. 
     Therefore, a need exists in the art for a method and apparatus for estimating three dimensional pose (rotation and translation) and three dimensional structure of unmodeled scenes to facilitate the authentic insertion of synthetic objects into a real scene view. 
     SUMMARY OF THE INVENTION 
     The present invention provides an apparatus and method of estimating pose and scene structure in extended scene sequences while allowing for the insertion and authentic projection of three dimensional synthetic objects into real views. Generally, given a video sequence of N frames, the invention computes the camera pose (the rotation and translation) without knowledge of a three dimensional model representing the scene. The inventive apparatus executes a multi-view three dimensional pose and structure estimation routine comprising the steps of feature tracking, pairwise camera pose estimation, computing camera pose for overlapping sequences and performing a global block adjustment that provides camera pose and scene geometric information for each frame of a video sequence. The pairwise camera pose estimation may alternately be selected between “key” frames rather than every frame of the sequence. The “key” frames are selected from within a sequence of frames, where the “key” frames are: frames with sufficient parallax motion between the frames; frames that transition between overlapping sets of correspondences; or frames that are regularly sampled if motion within the frame sequence is smooth. A “Match Move” routine may be used to insert a synthetic object into one frame of a video sequence based on the pose and geometric information of the frame, and calculate all other required object views of the synthetic object for the remaining frames using the pose and geometric information acquired as a result of the multi-view three dimensional estimation routine. As such, the synthetic object is inserted into the scene and appears as a “real” object within the imaged scene. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The teachings of the present invention can be readily understood by considering the following detailed description in conjunction with the accompanying drawings, in which: 
     FIG. 1 depicts a block diagram of an imaging apparatus incorporating the image analysis method and apparatus of the invention; 
     FIG. 2 is a flow chart of a first embodiment for the image analysis method of the invention used to estimate three dimensional pose and structure of an unmodeled scene; 
     FIG. 3 is a flow chart of the tracking procedure; 
     FIG. 4 is a flow chart of the pairwise camera pose estimation procedure; 
     FIG. 5 is a flow chart of the subsequence pose estimation procedure and the stitching procedure; 
     FIG. 6 shows three frames ( 1 ,  21 ,  32 ) of a 32 frame “garden” sequence; 
     FIG. 7 shows three frames ( 1 ,  8 ,  15 ) of a 15 frame “dorm” sequence; 
     FIG. 8 shows three frames ( 1 ,  11 ,  21 ) of a 50 frame “terrain” sequence; 
     FIG. 9 shows the average the Front End, Stitch and Global Minimization error distribution over the 32 frames of the “garden” sequence for the three processing stages; 
     FIG. 10 shows the average Front End, Stitch and Global Minimization error distribution over the 15 frames of the “dorm” sequence for the three processing stages; 
     FIG. 11 shows the average Front End, Stitch and Global Minimization error distribution over the 50 frames of the “terrain” sequence for the three processing stages; 
     FIG. 12 depicts pose estimates computed by the invention for the dorm sequence; 
     FIG. 13 depicts pose estimates computed by the invention for the garden sequence; 
     FIG. 14 depicts two frames of the Garden sequence in which some synthetic objects have been inserted; 
     FIG. 15 depicts two original frames of the Dorm sequence in which some synthetic objects have been inserted; 
     FIG. 16 depicts a VRML generated view of the texture mapped model of the dorm and the top view of the point cloud that is reconstructed; 
     FIG. 17 is a flow chart of the match move method of the invention used to insert a synthetic object into an unmodeled scene; 
     FIG. 18 is a flow chart of a second embodiment for the image analysis method of the invention used to estimate three dimensional pose and structure of an unmodeled scene; and 
     Table  1  is a summary of maximum and average errors over all the frames after the three stages of processing for three sequences. 
    
    
     To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. 
     DETAILED DESCRIPTION 
     FIG. 1 depicts a three dimensional multi-view estimation apparatus  100  of the present invention. An input video sequence  112  is supplied to a computer  102  that is comprised of a central processing unit (CPU)  104 , support circuits  106 , and memory  108 . Residing within the memory  108  is a multi-view three dimensional pose and structure estimation routine  110  and a match move routine  116 . The multi-view three dimensional estimation routine  110  and the match move routine  116  may alternately be readable from another source such as a floppy disk, CD, remote memory source or via a network. The computer  102  additionally is coupled to input/output accessories  118 . As a brief description of operation, an input video sequence  112  is supplied to the computer  102 , which, after operation of the multi-view three dimensional estimation routine  110 , outputs an output video sequence  114 . The output sequence  114  contains processed subsequences of frames from the input video sequence that form a long sequence that has been adjusted using a pose estimation to accurately combine the subsequences. If the insertion of a synthetic object into a real scene is desired, the match move routine  116  uses the result of the multi-view three dimensional estimation routine  110  to create the output video sequence  114  containing a plurality of input sequences plus a synthetic object. 
     FIG. 2 is a flow diagram of a first embodiment for the multi-view three dimensional estimation routine  110  for estimating pose and structure in extended scene sequences while allowing for the insertion and authentic projection of three dimensional synthetic objects into real views. FIG. 2 depicts the flow diagram of an input video sequence or subsequences  112  through the multi-view three dimensional estimation routine  110  to yield a output video sequence  114 . The multi-view three dimensional estimation routine  110  is illustrated as a series of procedures beginning with feature tracking  204  followed by pairwise camera pose estimation  206 , subsequence pose estimation  208 , stitching subsequences  210 , respectively terminating in a global adjustment  212  of the stitched subsequences. If the insertion of a synthetic object is desired, the match move routine  116  is performed after the global adjustment  212  to obtain the output video sequence  114  containing an accurate combination of the image subsequences. 
     The feature tracking procedure  204  is detailed in FIG.  3 . The feature tracking procedure  204  uses the frame-to-frame coherence of the input video sequence  112  having N frames for establishing good correspondences between pairs of frames. Since an assumption of the presence of specific structures in each frame the input video sequence  112  cannot be relied upon, and the ability to handle fairly arbitrary three dimensional motions are desired, a series of steps are executed to choose and track features within the frame sequence. The first step  302  selects features in frame set t and t+1. The frame set (or pair of frames) may be any pair of frames in a frame sequence. For example, the frames could be sequential or selected from the frame sequence as “key frames”. The key frames are: frames with sufficient parallax motion between the frames; frames that transition between overlapping sets of correspondences; or frames that are regularly sampled if motion within the frame sequence is smooth. Features are chosen on the basis of their contrast strength and distinctiveness with respect to their local pixel neighborhoods. The distinctiveness property is useful for selecting features in scenes where there may be a lot of repetitive structures and hence establishing unique correspondences may be a problem. 
     Salient point features are selected as centers of image patches whose auto-correlation surface satisfies a number of properties at a few different scales. Step  304  computes the auto-correlation surfaces at those locations where the average gradient magnitude within a window is above a chosen threshold (typically chosen to be 10.0). At each scale (level) in a Gaussian pyramid, the auto-correlation surface must satisfy the following properties: (i) it should be approximated well by a quadratic surface, (ii) it should be concave downwards, (iii) the minimum curvature magnitude should be above a threshold, and (iv) the ratio of the larger to the smaller curvature should be above a threshold. Checking these properties at a number of given scales ensures that high contrast points that are locally distinctive are selected. The specific thresholds used to select the initial feature points are not very critical since a number of processing stages are used to prune and further constrain good features to track. 
     In every new frame feature points are initialized as above and the features from a previous frame are projected into the new frame. Since neither the camera pose nor the three dimensional location of the features is known, both forward and reverse displacement fields are computed between pair t and t+1 of frames in step  306 . The displacement field is computed using a multi-resolution coarse-to-fine optic-flow algorithm disclosed in Bergen, et al., “Hierarchical Model-based Motion Estimation”, Proc. 2 nd  European Conference on Computer Vision (1992), pp. 237-252, or it may also be computed by constraining the displacements to lie on epipolar lines using a small-motion three dimensional model disclosed in Hanna, et al., “Combining Stereo And Motion Analysis For Direct Estimation Of Scene Structure”, Proc. Intl. Conf. on Computer Vision Workshop (1993), pp. 357-365. The forward and backward displacement vectors are used to check for flow consistency at a point within the frame. That is, a point is considered desirable for feature tracking if its predicted location using the forward and backward flow projection is consistent. That is, point p t  is flow consistent if δ(p t )=∥p pred −p t ∥≦ε, where p pred =p + +ub t+1 (p + ), p + =p t +uf t (p t ), p +  is the forward projection of p t  into the frame t+1 using the forward displacement field uf t , and p pred  is the backward projection of p +  into frame t using the backward displacement field ub t+1 . Note that ub t+1 (p + ) is an interpolated vector since p +  may not in general lie on a grid location. 
     In any given frame, both new points and points projected from a previous frame are checked for flow consistency at their locations in step  308 . Only points that are flow consistent are kept for further tracking from frame to frame. In step  310 , the routine checks if t+1=N. If t+1&lt;N, the process of steps  302 ,  304 ,  306  and  308  are repeated for the next incremental frame pair (t i  and t i +1). Once t+1=N, the pairwise camera pose estimation procedure  206  is performed. 
     As mentioned above, another method for feature tracking  204  is the use of key frames. Key frames are selected as frames with sufficient parallax motion between frames, as frames that transition between overlapping sets of correspondences, or frames that are regularly sampled if the motion within the frame sequence is smooth. 
     Pairwise camera pose estimation  206  is described in greater detail in FIG.  4 . Pairwise camera pose estimation  206  begins with step  402  where the initial estimates for the camera pose are computed using the fundamental matrix constraint disclosed in Hartley, et al., “Triangulation”, Proc. DARPA Image Understanding Workshop (1994), pp. 957-966. The tri-focal constraint may also be used disclosed in Torr, P. and Zisserman, A., “Robust Parameterization And Computation of the Trifocal Tensor”, Image and Vision Computation, Vol. 24 (1997), pp. 271-300. Given a point P w  in some world coordinate system, its projection in the fth frame can be written as: 
     
       
           p   f   ≈AM   f   P   w   (1) 
       
     
     where A=[[f x  0 0] T [0 f y  0] T [c x  c y  1] T ], is the camera calibration matrix with the scale factors and center coordinates in image pixels.            M   f     =     [                      R   f           T   f             0       1                    ]       ,                          
     is the 3×4 camera projection matrix with the rotation and translation for frame f. 
     Given point correspondences between a pair of frames, it is well known that the two frame projections constraint is captured by the fundamental matrix constraint: p f   T Fp f+1 =0. The fundamental matrix can be computed by a number of known techniques. 
     In the subsequent step  404 , outliers are rejected from the fundamental matrix using a least median squares minimization. After the outlier rejection of step  404 , the final F matrix is computed in step  406  using the image based error measure after outlier rejection. 
     With the knowledge of the approximately known camera calibration, the F matrix can be decomposed into the camera pose matrices M f  using a technique described in R. I. Hartley, “Estimation of Relative Camera Positions for Uncalibrated Cameras”, Proc. 2 nd  European Conference on Computer Vision, Pages 579-587, 1992. Note that in principal, using multiple frames and projective camera matrices, the self-calibration techniques also could be employed. However, first, the stability of these techniques under arbitrary three dimensional motions is not guaranteed, and second, in practice, the only internal camera parameter that needs adjustment beyond the roughly known parameters is the focal length or scale. Aspect ratio, skew and the principal point are either generally known or nominal parameters are adequate. The final step  410  in pairwise camera pose estimation  206  assumes standard values for aspect ratio, skew and principal point and uses a rough initial guess of the focal length to convert from projective camera matrices to Euclidean coordinates. The focal length parameter is adjusted further in the maximum-likelihood stage that is chosen to be some factor of the image. 
     Alternately, the pairwise camera pose estimation  206  may be accomplished by executing a step  412  in place of steps  408  and  410 . Step  412  directly computes the Euclidean pose from the final F matrix of step  406 . 
     In order to exploit a static rigid scene constraint, the pairwise camera estimates of procedure  206  are used to create consistent camera pose estimates and the corresponding three dimensional point locations over short subsequences in the subsequence pose estimation procedure  208 . The advantage of dividing video sequence into subsequence estimates is threefold: (i) better computational efficiency since global bundle block adjustment for all frames is expensive, (ii) progressively better generation of estimates leading to a global maximum likelihood estimation, and (iii) limited build up of error due to small scale concatenation of local pose estimates. Subsequence pose estimation  208  is described in greater detail in FIG.  5 . 
     Subsequence pose estimation  208  begins with step  502  which creates a subsequence from the output of the pairwise camera pose estimation process  206 . Point tracks that persist for the time period of each subsequence are used to create the consistent estimates. For a subsequence 0 to s, say 0 is chosen as the reference frame. The pairwise camera estimation is performed for each pair 0−f with f≦s. Therefore, for each subsequence the camera transformations benefit from varying baseline between pairs of frames. For subsequence processing, the pairwise estimates first need to be represented consistently in the fth frame&#39;s coordinate system. This can be achieved by solving for an unknown scale factor for each frame. That is P f =R f P 0 +α f T f  where P 0  and α f  are the unknowns, P 0 &#39;s are the three dimensional points in the 0th frame and α f  is the scale factor for the fth frame in the subsequence. One of the α&#39;s is arbitrarily set to unity. The rest of the scale factors and all the three dimensional points can be solved by minimizing the image projection errors or the triangulation errors. Essentially the sum of image reprojection errors, E=Σ n Σ f ∥p fn −Π(P fn )∥ 2 , is minimized over all three dimensional points and frames indexed by n and f, respectively, using iterative least squares (Σ is the three dimensional to two dimensional projection operator). The output of this process is a collection of consistent camera poses and a set of reconstructed three dimensional points all of which are visible in the subsequence 0 to s. 
     However, the poses obtained above are not optimal since only the overall scale factors have been adjusted in the optimization process. In order to compute the maximum likelihood estimates for the camera pose and three dimensional points for the subsequence, a bundle block adjustment is applied in step  504  to optimize the subsequence. The following maximum likelihood criterion is minimized:        E   =       ∑   f                       ∑   n                                       p     0   ,   n       (   3   )       -     T   f                          p     0   ,   n       (   3   )         -       T   f                        -       R   f     ·       p     f   ,   n       (   3   )                        p     f   ,   n       (   3   )                                                 2                                
     Essentially the three dimensional points P 0,n  are projected as unit vectors in the fth frame, and the corresponding observed image points, P f,n  are also represented as unit vectors. The error measure is the distance between the two unit vectors summed over all the three dimensional points and frames (including the reference frame) indexed by n and f, respectively. The advantage of this representation is that the images could each be a spherical mosaic representation. The unit vector representations can handle both planar and wide angle projections. The calibrations parameters also are folded into the image vector, P f,n  as in Eqn. 1. Therefore, the scale factor and other parameters can be adjusted within the same optimization. 
     Bundle block adjustment exploits the nice block structure that the system of normal equations has for the above sum of squares problem. The LHS matrix looks like:                       [           M   1                     0         N   11         ⋯         N     1   ,   P                           ⋰                   ⋮       ⋰       ⋮           0                     M     F   -   1             N       F   -   1     ,   1                         N       F   -   1     ,   P                 N   11   T         ⋯         N       F   -   1     ,   P     T           S   1         ⋯       0           ⋮       ⋰       ⋮                   ⋰                         N     1   ,   P     T         ⋯         N       F   -   1     ,   P     T         0                     S   P           ]                                            
     The M f &#39;s are the Hessian matrices (with the standard approximation for a sum-of-squares problem) with respect to the fth camera parameters, and thus represent the summation over all the points visible in frame f, Likewise, S n &#39;s are the Hessians with respect to the nth three dimensional point and are summed over all the frames in which that point is visible. The off-diagonal blocks, N f,n  matrices corresponding to the cross derivatives with respect to the fth frame and the nth three dimensional point. Furthermore, the incremental solution for the three dimensional points can be analytically computed in terms of the solutions of the camera parameters. Therefore, by back-substitution, one never has to store the full k*(F−1)+3*P matrix where k are the camera parameters per frame, F is the number of frames, and P is the number of points. 
     The process of creating a subsequence  502  and optimizing the subsequence is checked at step  506  to ensure that the entire sequence obtained from the pairwise pose estimation process  206  is processed into subsequences. Portions of the sequence from the pairwise pose estimation process  206  which have not undergone steps  502  and  504  are looped back though steps  502  and  504  by step  508  until a string of subsequences is passed on to the stitching process  210 . 
     The stitching process  210 , also detailed in FIG. 5, begins with step  602  which performs a subsequence computation to select a few frames which overlap between consecutive subsequences. Step  604  takes the points that are visible in two overlapping subsequences and uses them to combine the subsequences to represent both the subsequences in a common coordinate system. A common three dimensional point has two different representations in two subsequences: P s1  and P s2  but the same image frame is used in the two subsequences. Then the image projection can be written in two ways in Eqn. 1, one with the camera matrix with the pose {R 1 T 1 } and the other with the pose {R 2 T 2 }. Therefor, there is a similarity transformation corresponding to {s,R,T} such that the two three dimensional representations of each point and the two camera representations of the same frame are identical. That is, from Eqn. 1, M s1 =M s2 S −1 , and [P s1 1] T =S[P s2 1] T . The unknown similarity transformation S is computed by minimizing an image based error measure:            ∑   n                       d   2          (       p     n   ,            Π        (       M   s1            S        [       P     s2   ,   n          1     ]       T       )         )         +       d   2          (       p     n   ,            Π        (       M   s2              S     -   1            [       P     s1   ,   n          1     ]       T       )         )                              
     The steps  606  and  608  function to repeat the steps  602  and  604  until all of the subsequences obtained from the subsequence pose estimation procedure  208  are recombined into a single sequence and are ready for the global adjustment procedure  212 . 
     The stitching subsequences procedure  210  allows the representation of pose and the three dimensional points in a single coordinate system. However, point tracks that are common across more than one subsequence provide constrains for further global adjustment of the three dimensional parameters. In the final global adjustment procedure  212 , bundle block adjustment as described for subsequence pose estimation procedure  208 , is applied to the complete set of frames and three dimensional points. For computational efficiency, this adjustment can be applied in small sets of arbitrary frames or can be applied to the complete set. The interesting aspect of the representation here is that any combination of internal parameters, pose parameters or three dimensional points can be adjusted while maintaining a global representation. 
     The performance of this approach is shown both quantitatively and in terms of the visual stability of three dimensional object insertion. Three video sequences that are representative of three different types of motions that may be used in three dimensional model acquisition and in video post-production type applications. The first has motion mostly in the view direction (T 2 ) with rotations and change of motion, the second is a fixation type motion, and the third is almost pure translatory motion parallel (T y ) to the image plane simulating an aerial platform. For quantitative results, the reprojection errors (the difference in pixels in the location of a projected three dimensional point and its measured image location) were compared for each frame at various stages of processing. The processing stages are abbreviated as: (i) FE: the stage, called the FrontEnd, at which the pairwise camera estimates with respect to a reference in a sub-sequence are combined using a common scale factor, (ii) Stitch: where the overlapping sub-sequences are stitched together and represented in a consistent coordinate system using similarity transformations, and (iii) Gbl Min : the final global adjustment that combines all the three dimensional point and pose estimates in one optimization. For each of the three video sequences, plots of the average errors for each frame over all the visible points in that frame are shown. 
     The first sequence, called garden, is a sequence of 97 frames that are sub-sampled by 3 to create a sequence of length 32. The size of each frame is 1024×768. The field of view is about 45° and the camera moves first mostly along the view axis and then turns sideways. Three frames of the sequence (frames  1 ,  21 , and  32  ) are shown in FIG.  6 . (There are some markers put on the fence but these markers were not used for feature tracking.) It is to be emphasized that there are not many visually distinctive features in this sequence. Using the auto-correlation and flow-based tracker, a total of 757 points were tracked over the 32 frames with the number per frame ranging from 200 to 30. After outlier removal, finally, 732 points were used for the global adjustment. 
     The distribution of the average reprojection error distribution over the 32 frames of the garden sequence for the processing steps  208 ,  210 , and  212  are shown in FIG.  9 . The axis  130  and axis  134  represents the error while axis  132  and axis  136  represents the frame number. Plot  138  is the average FE error, plot  140  represented by a solid line is the average Stitch error, and the plot  142  represented by a dotted line is thee average Gib Min error. 
     The highlight of the results is the progressive reduction in reprojection errors. Consider the mean error in each frame. The maximum value of the mean error per frame reduces from 3.429 pixels after final matrix computation to 0.482 pixels after stitch, and further to 0.262 after global minimization. Likewise, the mean of the mean errors per frame goes down from 0.646 pixels to 0.188 pixels and finally to 0.139 pixels. 
     The pose estimates that are computed by the invention for the garden sequence are depicted as camera coordinates in VRML are shown in FIG.  13 . The x axis is denoted by  260 , the y axis is denoted by  264 , and the z axis is denoted by  262 . 
     The second sequence is a 15-frame sequence of a university dormitory (dorm) that was captured as 640×480 frames using a digital camera. The camera field of view was about 50°. Three frames of the sequence (frames  1 ,  8 , and  15  ) are shown in FIG.  7 . The camera was moved around the building to keep the building in view and hence this is an example of a fixation like sequence, the type of sequence that is considered ideal for model acquisition. A total of 431 points were tracked with points in each frame ranging from 81 to 241. Again, the maximum value of the mean error per frame reduces from 4.125 pixels after final matrix computation to 1.003 after stitch, and further to 0.420 pixels after global minimization. Likewise, the mean of the mean errors per frame goes down from 2.008 pixels to 0.450 and finally to 0.297 pixels. 
     The distribution of the average reprojection error distribution over the 15 frames of the dorm sequence for the processing steps  208 ,  210 , and  212  are shown in FIG.  10 . The axis  150  and axis  152  represents the error, while axis  154  and axis  156  represents the frame number. Plot  158  is the average FE error, plot  160  represented, by a solid line is the average Stitch error, and the plot  162  represented by a dotted line is the average Glb Min error. 
     The pose estimates that are computed by the invention for the dorm sequence are depicted as camera coordinates in VRML are shown in FIG.  12 . The x axis is denoted by  250 , the y axis is denoted by  254 , and the z axis is denoted by  252 . 
     The third sequence is a 50-frame sequence of a terrain model captured using a camcorder attached to a gantry to simulate aerial motion. The camera was zoomed in so that the field of view was only about 10°. Unfortunately, the gantry set up is only able to move the camera but not provide any pose information. The video was digitized to 320×240 frames and was temporally sampled by 4. Therefore the effective length of the sequence is 200 frames. Three frames of the sequence (frames  1 ,  11 , and  21 ) are shown in FIG.  8 . As before, a maximum error averaged over all the 50 frame of 5.563 pixels after final matrix computation reduces to 1.788 pixels after stitch and to 0.353 pixels after global minimization. The mean of the average frame errors for the three stages processing are 1.661, 0.427 and 0.220 pixels respectively. 
     The distribution of the average reprojection error distribution over the 50 frames of the terrain sequence for the processing steps  208 ,  210 , and  212  are shown in FIG.  11 . The axis  170  and axis  172  represents the error, while axis  174  and axis  176  represents the frame number. Plot  178  is the average FE error, plot  180  represented by a solid line is the average Stitch error, and the plot  182  represented by a dotted line is the average Glb Min error. 
     It is well known that two-frame estimates in such an imaging situation suffer from an ambiguity between rotations and the depth scale (and translation), the bas-relief ambiguity. However, the effective field of view is enhanced by our multi-frame tracking and local-to-global optimization strategy, and hence reliable results are obtained even in this situation. 
     The maximum, average and median errors for the average errors over all the frames for the three sequences are summarized in Table 1: 
     To emphasize that not only is the three dimensional pose of the camera computed, but the tracked set of points are also accurately reconstructed, FIG. 16 is provided as an example of texture mapped model. FIG. 16 shows a VRML generated view of the texture mapped model of the dorm and the top view of the point cloud that is reconstructed. The texture mapping is accomplished by defining triangles over one reference image. Note that both three dimensional poses and points are recovered well. 
     Accurate and stable pose estimation important for the application of three dimensional match move in which synthetic objects are inserted into real images and their viewpoints are mimicked according to the real camera&#39;s pose and internal parameters. The match move routine  116  is employed when desired after the global adjustment procedure  212 . The match move routine  116  is detailed in FIG.  17 . 
     To initiate the match move routine  116 , a synthetic object  702  and camera pose in the coordinate system developed by the multi-view estimation routine  110  must be supplied. The step  704  computes the object view for the synthetic object  702  based on the camera pose information obtained from the multi-view estimation routine  110  for the frame into which the synthetic object  702  is to be inserted. The synthetic object  702  is then inserted (or pasted) into the frame in step  706  based on the object view for that frame. Step  708  determines if the synthetic object  706  is to appear in the next frame. If the synthetic object  702  is to appear in the next frame, the object view for the next frame is updated in step  710  and the steps of pasting object view  706  and determining  708  if the synthetic object  702  is in subsequent frames is repeated. If the synthetic object  702  is not to be present in the next frame, the match move routine is finished resulting in the output video sequence  114 . 
     The effects of the match move routine  116  on the output video sequence  114  are depicted with the illustrations below. 
     FIG. 14 shows two original frames of the Garden sequence in which some synthetic objects have been inserted. The object was inserted in the three dimensional coordinates of the scene points of the first camera frame created by the algorithm and in the coordinate system developed by the multi-view estimation routine  110  of one camera. In order to situate the object precisely some local surface modeling was performed using the three dimensional estimates obtained. Once the synthetic object was placed with respect to the real surface in the reference frame, all other views of the object are created using editing software and the computed camera transformations. Editing software, such as OpenInventor, is available from SGI. 
     A similar insertion for the Dorm sequence is shown in FIG.  15 . It is to be emphasized that in both the data sets, the placement of the synthetic images has been accomplished with respect to one frame only and that too with respect to the three dimensional surface computed by the algorithm. Therefore, there is minimal interaction demanded of the user. It is to be emphasized that both in the still image displays as well as in the videos, no drift or jitter in the objects in any of the frames is noticeable. This is a qualitative visual validation of the stability of pose and three dimensional structure computation of the algorithm developed. 
     FIG. 18 depicts a second embodiment of the multi-view three dimensional estimation routine  110 . The second embodiment of multi-view three dimensional estimation routine  110  is particularly useful when the reference imagery and the current imagery is significantly different or does not contain a significant number of image features. Since the camera parameters can be itteratively resolved and refined, creating accurate estimations, the second embodiment of the multi-view three dimensional estimation routine  110  achieves excellent results from a single frame. First the feature tracking step  204  is performed (step  204  is described above with respect to FIG.  3 ). Next, in step  802 , an approximate transform between the current and reference imagery is provided by a separate coarse search module, by ESD (Engineering Support Data) or by an initialization algorithm. The output of the frame-to-frame processing is the transformation between successive current images. These are fed into a global minimization algorithm disclosed in Sawhney, et al., “Robust Video Mosaicing Through Topology Inference And Local to Global Alignment”, ECCV (1998), pp. 103-119, that minimizes the error between virtual points that are created using the frame to frame parameters and the coarse search parameters to recover an estimate of the transform between each of the current images and the reference imagery. 
     The next step  804  uses the image matches between the current and reference imagery at each frame to improve accuracy. This is performed by computing point correspondences using a hierarchical flow algorithm, see Bergen, et al., pp. 237-252. To compensate for the algorithm assuming brightness consistency, the images are pre-filtered with Laplacian filters in order to reduce the impact of illumination changes. The algorithm computes local matches at every image pixel, first at course resolution, then refining the matches at finer resolutions. 
     These point matches are then sampled and, together with the frame to frame parameters, are processed by the global minimization algorithm in step  806 , see Sawhney, et al., pp. 103-119. The result is a set of refined parameters for each frame. These parameters are then used to warp or shift the reference imagery to each current image in the match move step  116 . Point matches between the warped reference imagery and the current imagery can be re-computed iteratively in step  808  to refine the model parameters to a greater level. 
     Although the embodiment which incorporate the teachings of the present invention have been shown and described in detail herein, those skilled in the art can readily devise many other varied embodiments that still incorporate these teachings.