Reconstruction of shapes of objects from images

• US 8,224,065 B2
• Filed: 01/09/2008
• Issued: 07/17/2012
• Est. Priority Date: 01/09/2007
• Status: Expired due to Fees

• Current Assignee: Purdue Research Foundation (The Trustees of Purdue University (d/b/a Purdue University))
• Original Assignee: Purdue Research Foundation (The Trustees of Purdue University (d/b/a Purdue University))
• Inventors: Pizlo, Zygmunt, Li, Yunfeng, Steinman, Robert M.
• Primary Examiner(s): Desire, Gregory M
• Application Number:
  US11/971,898
• Publication Number:
  US 20080166065A1
• Time in Patent Office: 1,651 Days
• Field of Search: 345/419, 382/154, 382/285
• US Class Current: 382/154
• CPC Class Codes: G06T 7/543 from line drawings
• CPC Class Codes: G06V 10/10 Image acquisition document image scanning and transmission H04N1/00; control of digital cameras H04N23/60
• CPC Class Codes: G06V 2201/12 Acquisition of 3D measurements of objects

Abstract

The present disclosure describes a system and method for transforming a two-dimensional image of an object into a three-dimensional representation, or model, that recreates the three-dimensional contour of the object. In one example, three pairs of symmetric points establish an initial relationship between the original image and a virtual image, then additional pairs of symmetric points in the original image are reconstructed. In each pair, a visible point and an occluded point are mapped into 3-space with a single free variable characterizing the mapping for all pairs. A value for the free variable is then selected to maximize compactness of the model, where compactness is defined as a function of the model'"'"'s volume and its surface area. “Noise” correction derives from enforcing symmetry and selecting best-fitting polyhedra for the model. Alternative embodiments extend this to additional polyhedra, add image segmentation, use perspective, and generalize to asymmetric polyhedra and non-polyhedral objects.

Claims

1. A system, comprising a processor and a memory in communication with the processor, the memory storing programming instructions executable by the processor to:

accept input of a two-dimensional image of a 3-D object that is symmetric about a plane, the 3-D object having a surface;

accept input of three pairs of points, each pair being visible in the image, on the surface of the object, and symmetric about the plane;

these three pairs of points are not all coplanar in the 3D model;

create a model of the object by

reconstructing coordinates in 3-space for each point in the three pairs of points in terms of exactly one free parameter;

determining coordinates in 3-space of at least one additional pair of symmetric points on the surface of the object, wherein

each additional pair includes at least one point that is visible in the image, and

each additional pair is specified in terms of the free parameter; and

selecting a value for the free parameter that at least approximately maximizes a compactness metric as applied to the reconstructed object in 3-space; and

provide output that encodes the model.

2. The system of claim 1, wherein the compactness metric is a function of the one free parameter representing the stretch of the model in the dimension that is being reconstructed from the image.

3. The system of claim 1, wherein the compactness metric is the ratio of the volume of the model raised to power m, to the surface area of the model raised to power n (V^m/Sⁿ), where m<

n.

4. The system of claim 3, wherein m=2, and n=4.8.

5. The system of claim 1, wherein the compactness metric is a function of the surface area of the model.

6. The system of claim 1, wherein the selected value for the free parameter exactly maximizes the compactness metric as applied to the model.

7. The system of claim 1, wherein the programming instructions are further executable by the processor to perform image segmentation on the input image to automatically detect vertices to serve as the three pairs of points.

8. The system of claim 1, wherein the compactness metric is a function of the ratio of

the total volume raised to power m, of one or more regular polyhedra that collectively circumscribe the object to

the total surface area raised to power n, of the one or more regular polyhedra,

where m<

n.

9. The system of claim 8, wherein m=2, and n=4.8.

10. The system of claim 1, wherein the selecting function comprises generating the model from polyhedra, including selecting a best-fit polyhedron using a fit optimization method.

11. A method of extracting three-dimensional structure of an object from a two-dimensional image of the object, where the object is symmetric about a plane, comprising:

creating a model of the object in 3-space by

automatically reconstructing coordinates in 3-space for each point in three pairs of symmetric points in terms of exactly one free parameter;

automatically determining coordinates in 3-space of at least one additional pair of symmetric points on the surface of the object, wherein

each additional pair includes at least one point that is visible in the image, and

each additional pair is specified in terms of the free parameter; and

automatically selecting a value for the free parameter that at least approximately maximizes a compactness metric as applied to the model; and

generating an output signal that encodes the model.

12. The system of claim 11, wherein the compactness metric is the ratio of the square of the volume of the model to a power of the surface area of the model, where the power is greater than two.

13. The system of claim 11, wherein the compactness metric is a function that varies inversely with the surface area of the model.

14. The system of claim 11, wherein the selected value for the free parameter exactly maximizes the compactness metric as applied to the model.

15. The system of claim 11, wherein the programming instructions are further executable by the processor to perform image segmentation on the input image to automatically detect vertices to serve as the three pairs of points.

16. The system of claim 11, wherein the compactness metric is a function of the ratio of

the square of the total volume of one or more regular polyhedra that collectively circumscribe the object to

a power of the total surface area of the one or more regular polyhedra,

where the power is greater than two.

17. The system of claim 11, wherein the selecting function comprises generating the model from polyhedra, including selecting a best-fit polyhedron using a fit optimization method.

18. The system of claim 11, wherein the automatically selecting step comprises generating the model from polyhedra, including selecting a best-fit polyhedron using a fit optimization method.

19. A system for producing a three-dimensional model of an object from a two-dimensional image, comprising a processor and a memory, the memory storing programming instructions executable by the processor to:

accept input of a two-dimensional image of a 3-D object that is symmetric about a plane, the 3-D object having a surface;

accept input of one or more pairs of points, each pair being visible in the image, on the surface of the object, and symmetric about the plane;

create a model of the object by

reconstructing coordinates in 3-space for each point in the one or more pairs of points;

determining coordinates in 3-space of at least one additional pair of symmetric points on the surface of the object, wherein each additional pair includes at least one point that is visible in the image; and

moving a point from the one or more pairs or from the at least one additional pair of points to enforce symmetry about the plane; and

output a signal that encodes the model.

20. The system of claim 19, wherein the programming instructions are further executable by the processor to perform image segmentation on the input image to automatically detect vertices to serve as the one or more pairs of points.