Compact multi-aperture three-dimensional imaging system

1. A method for designing a passive multiview-imaging system based on a sensor array and a processor, the sensor array divided into partitions, each partition forming a sensor area for a corresponding imaging subsystem, each imaging subsystem capturing an image of an object, the processor estimating depth in the object based on the captured images, the design method comprising a computer system performing the steps of:

automatically generating a plurality of different candidate partitions, each candidate partition defining a division of the sensor array into two or more sensor areas, wherein at least some of the candidate partitions divide the sensor array into three or more sensor areas;

estimating a quality metric for each candidate partition, comprising:

accessing an optical design for the corresponding imaging subsystems for the candidate partition;

modeling the image capture by the imaging subsystems and the depth estimation by the processor, the modeling taking into account the input specifications; and

estimating the quality metric based on the modeled depth estimation; and

ordering the candidate partitions according to their estimated quality metrics.

2. The method of claim 1 wherein all of the automatically generated candidate partitions divide the sensor array into a regular array of sensor areas.

3. The method of claim 2 wherein all of the automatically generated candidate partitions divide the sensor array into a rectangular array of sensor areas.

4. The method of claim 1 wherein at least one of the automatically generated candidate partitions divides the sensor array in a manner that is not a regular array of sensor areas.

5. The method of claim 1 wherein the step of the computer system automatically generating a plurality of different candidate partitions generates only candidate partitions that are consistent with manufacturability constraints in the input specifications.

6. The method of claim 1 wherein a majority of the candidate partitions divide the sensor array into three or more sensor areas.

7. The method of claim 1 wherein not more than one of the candidate partitions divides the sensor array into two sensor areas.

8. The method of claim 1 wherein the quality metric comprises an error in a three-dimensional position estimation.

9. The method of claim 1 wherein the quality metric comprises an uncertainty in a three-dimensional position estimation.

10. The method of claim 1 wherein the step of estimating the quality metric comprises:

using a closed form solution to estimate a three-dimensional position (X,Y,Z) of an object.

11. The method of claim 1 wherein the step of estimating the quality metric comprises:

using a Monte Carlo approach to estimate uncertainty in a three-dimensional position (X,Y,Z) of an object.

12. The method of claim 1 wherein the step of the computer system estimating the quality metric comprises:

estimating a disparity of the candidate partition; and

estimating the quality metric based on the disparity.

13. The method of claim 1 wherein the optical designs for all of the imaging subsystems are the same.

14. The method of claim 1 wherein the optical designs for all of the imaging subsystems are variations of a common base optical design.

15. The method of claim 1 wherein, for at least two of the imaging subsystems, their corresponding optical designs have different spectral responses, different transfer functions and/or different polarization responses.

16. The method of claim 1 wherein the step of estimating a quality metric for each candidate partition further comprises:

the computer system modifying the depth estimation by the processor to improve the quality metric.

17. The method of claim 1 wherein the step of ordering the candidate partitions comprises:

the computer system identifying the candidate partition with the best quality metric.

18. The method of claim 1 wherein the step of ordering the candidate partitions comprises:

the computer system rank ordering the candidate partitions by quality metric.

19. The method of claim 1 wherein the step of ordering the candidate partitions comprises:

the computer system presenting the quality metric as a function of an aspect of the candidate partitions.

20. A passive polyview-imaging system for estimating depth in an object, the polyview-imaging system comprising:

a sensor array, the sensor array divided into at least two sensor areas;

a plurality of imaging subsystems, each imaging subsystem comprising one of the sensor areas and corresponding optics, each imaging subsystem capturing an image of an object; and

a processor coupled to the sensor array, for estimating depth in the object based on the captured images;

wherein the passive polyview-imaging system is produced by a process comprising a computer system performing the steps of:

automatically generating a plurality of different candidate partitions, each candidate partition defining a division of the sensor array into two or more sensor areas;

estimating a quality metric for each candidate partition, comprising:

accessing an optical design for the corresponding imaging subsystems for the candidate partition;

modeling the image capture by the imaging subsystems and the depth estimation by the processor; and

estimating the quality metric based on the modeled depth estimation; and

ordering the candidate partitions according to their estimated quality metrics.