Virtual 3D methods, systems and software

1. A video communication method that enables a user to view a remote scene in a manner that gives the user a visual impression of being present with respect to the remote scene, the method comprising:

capturing images of the remote scene, the capturing comprising utilizing at least two cameras each having a view of the remote scene;

executing a feature correspondence function by detecting common features between corresponding images captured by the respective cameras and measuring a relative distance in image space between the common features, to generate disparity values, wherein the feature correspondence function utilizes a disparity histogram-based method of integrating data and determining correspondence, and wherein the disparity histogram-based method comprises constructing at least two disparity histograms per a given set of pixels, for use in evaluating validity of disparity values;

generating a data representation, representative of the captured images and the corresponding disparity values;

reconstructing a synthetic view of the remote scene, based on the representation; and

displaying the synthetic view to the first user on a display screen used by the first user;

the capturing, detecting, generating, reconstructing and displaying being executed such that:

(a) the user is provided the visual impression of looking through his display screen as a physical window to the remote scene, and

(b) the user is provided an immersive visual experience of the remote scene;

wherein utilizing a disparity histogram-based method comprises constructing at least two disparity histograms indicating the relative probability of a given disparity value being correct for a given pixel;

wherein at least one of the disparity histograms functions as a Probability Density Function (PDF) of disparity for the given pixel, in which higher values indicate a higher probability of the corresponding disparity range being valid for the given pixel; and

wherein one axis of at least one of the disparity histograms indicates a given disparity range, and a second axis of the at least one disparity histogram indicates the number of pixels in a kernel surrounding the central pixel in question that are voting for the given disparity range.

2. The method of claim 1 wherein votes indicated by at least one of the disparity histograms are initially generated utilizing a Sum of Square Differences (SSD) method.

3. The method of claim 2 wherein utilizing an SSD method comprises:

executing an SSD method with a relatively small kernel to produce a fast dense disparity map in which each pixel has a selected disparity that represents the lowest error;

then, processing a plurality of pixels to accumulate into the at least one disparity histogram a tally of the number of votes for a given disparity in a relatively larger kernel surrounding the pixel in question.

4. The method of claim 1 further comprising transforming the at least one disparity histogram into a Cumulative Distribution Function (CDF) from which the width of a corresponding interquartile range can be determined, thereby to establish a confidence level in the corresponding disparity solution.

5. The method of claim 2 further comprising maintaining a count of the number of statistically significant modes in the at least one histogram, thereby to indicate modality.

6. The method of claim 5 wherein modality is utilized as an input to reconstruction, to control application of stretch vs. slide reconstruction method.

7. The method of claim 2 further comprising:

generating fast disparity estimates for multiple independent axes; and then

combining corresponding, respective disparity histograms to produce a statistically more robust disparity solution.

8. The method of claim 2 further comprising:

extracting sub-pixel disparity information from the at least one disparity histogram, the extracting comprising:

where the at least one histogram indicates a maximum-vote disparity range and an adjacent, runner-up disparity range, calculating a weighted average disparity value based on the ratio between the number of votes for each of the adjacent disparity ranges.

9. The method of claim 1 wherein the capturing comprises utilizing a view vector rotated camera configuration wherein:

the locations of first and second cameras define a line; and

the line defined by the first and second camera locations is rotated by a selected amount from a selected horizontal or vertical axis;

thereby increasing the number of valid feature correspondences identified in typical real-world settings by the feature correspondence function.

10. The method of claim 9 wherein the first and second cameras are positioned relative to each other along epipolar lines.

11. The method of claim 9 wherein any of the captured images, the synthetic view or the disparity values are rotated back to a selected horizontal or vertical orientation.

12. The method of claim 1 wherein the feature correspondence function comprises evaluating and combining vertical- and horizontal-axis correspondence information.

13. The method of claim 12 wherein the feature correspondence function further comprises applying, to image pixels containing a disparity solution, a coordinate transformation, to a unified coordinate system.

14. The method of claim 13 wherein the unified coordinate system is the un-rectified coordinate system of the captured images.

15. The method of claim 12 further comprising utilizing at least three cameras arranged such that a pair of cameras is presented along a first axis and a second pair of cameras is presented along a second axis angularly displaced from the first axis.

16. A digital processing system for enabling a first user to view a remote scene with the visual impression of being present with respect to the remote scene, the digital processing system comprising:

at least two cameras, each having a view of the remote scene;

a display screen for use by the first user; and

a digital processing resource comprising at least one digital processor, the digital processing resource being operable to:

capture images of the remote scene, utilizing the at least two cameras;

execute a feature correspondence function by detecting common features between corresponding images captured by the respective cameras and measuring a relative distance in image space between the common features, to generate disparity values, wherein the feature correspondence function utilizes a disparity histogram-based method of integrating data and determining correspondence, and wherein the disparity histogram-based method comprises constructing at least two disparity histograms per a given set of pixels, for use in evaluating validity of disparity values;

generate a data representation, representative of the captured images and the corresponding disparity values;

reconstruct a synthetic view of the remote scene, based on the representation; and

display the synthetic view to the first user on the display screen;

the capturing, detecting, generating, reconstructing and displaying being executed such that:

(a) the first user is provided the visual impression of looking through his display screen as a physical window to the remote scene, and

(b) the first user is provided an immersive visual experience of the remote scene;

wherein utilizing a disparity histogram-based method comprises constructing at least two disparity histograms indicating the relative probability of a given disparity value being correct for a given pixel;

wherein at least one of the disparity histograms functions as a Probability Density Function (PDF) of disparity for the given pixel, in which higher values indicate a higher probability of the corresponding disparity range being valid for the given pixel; and

wherein one axis of at least one of the disparity histograms indicates a given disparity range, and a second axis of the at least one disparity histogram indicates the number of pixels in a kernel surrounding the central pixel in question that are voting for the given disparity range.

17. A program product for use with a digital processing system, for enabling a first user to view a remote scene with the visual impression of being present with respect to the remote scene, the digital processing system comprising at least two cameras, each having a view of the remote scene, a display screen for use by the first user, and a digital processing resource comprising at least one digital processor, the program product comprising digital processor-executable program instructions stored on a non-transitory digital processor-readable medium, which when executed in the digital processing resource cause the digital processing resource to:

capture images of the remote scene, utilizing the at least two cameras;

execute a feature correspondence function by detecting common features between corresponding images captured by the respective cameras and measuring a relative distance in image space between the common features, to generate disparity values, wherein the feature correspondence function utilizes a disparity histogram-based method of integrating data and determining correspondence, and wherein the disparity histogram-based method comprises constructing at least two disparity histograms per a given set of pixels, for use in evaluating validity of disparity values;

generate a data representation, representative of the captured images and the corresponding disparity values;

reconstruct a synthetic view of the remote scene, based on the representation; and

display the synthetic view to the first user on the display screen;

the capturing, detecting, generating, reconstructing and displaying being executed such that:

(a) the first user is provided the visual impression of looking through his display screen as a physical window to the remote scene, and

(b) the first user is provided an immersive visual experience of the remote scene;

wherein utilizing a disparity histogram-based method comprises constructing at least two disparity histograms indicating the relative probability of a given disparity value being correct for a given pixel;

wherein at least one of the disparity histograms functions as a Probability Density Function (PDF) of disparity for the given pixel, in which higher values indicate a higher probability of the corresponding disparity range being valid for the given pixel; and

wherein one axis of at least one of the disparity histograms indicates a given disparity range, and a second axis of the at least one disparity histogram indicates the number of pixels in a kernel surrounding the central pixel in question that are voting for the given disparity range.