METHOD AND SYSTEM FOR PROVIDING THREE-DIMENSIONAL AND RANGE INTER-PLANAR ESTIMATION

• US 20110181704A1
• Filed: 10/11/2009
• Published: 07/28/2011
• Est. Priority Date: 10/06/2008
• Status: Active Grant

• Current Assignee: Mantis Vision, Ltd.
• Original Assignee: Mantis Vision, Ltd.
• Inventors: Duadi, Hamootal, Negry, Shabtay, Bittan, Gur Arieh, Gordon, Eyal, Zalevsky, Zeev
• Granted Patent: US 8,908,016 B2
• Time in Patent Office: Days
• US Class Current: 348/49
• CPC Class Codes: G01B 11/002 for measuring two or more coordinates
• CPC Class Codes: G01B 11/2509 Color coding
• CPC Class Codes: G06T 7/521 from laser ranging, e.g. using interferometry; from the projection of structured light

Abstract

A system, apparatus and method of performing 3-D object profile inter-planar estimation and/or range inter-planar estimation of objects within a scene, including: providing a predefined finite set of distinct types of features, resulting in feature types, each feature type being distinguishable according to a unique bi-dimensional formation; providing a coded light pattern having multiple appearances of the feature types; projecting the coded light pattern, having axially varying intensity, on objects within a scene, the scene having at least two planes, resulting in a first plane and a second plane; capturing a 2-D image of the objects having the projected coded light pattern projected thereupon, resulting in a captured 2-D image, the captured 2-D image including reflected feature types; determining intensity values of the 2-D captured image; and performing 3-D object profile inter-planar estimation and/or range inter-planar estimation of objects within the scene based on determined intensity values.

Claims

1. A method of performing 3-D (three-dimensional) object profile inter-planar estimation and/or range inter-planar estimation of one or more objects within a scene, said method comprising:

a) providing a predefined finite set of distinct types of features, giving rise to feature types, each feature type being distinguishable according to a unique hi-dimensional formation;

b) providing a coded light pattern comprising multiple appearances of said feature types;

c) projecting said coded light pattern, having axially varying intensity, on one or more objects within a scene, said scene having at least two planes, giving rise to a first plane and to a second plane;

d) capturing a 2-D (two-dimensional) image of said objects having said projected coded light pattern projected thereupon, thereby giving rise to a captured 2-D image, said captured 2-D image comprising reflected said feature types;

e) determining intensity values of the 2-D captured image, giving rise to the determined intensity values; and

f) performing 3-D object profile inter-planar estimation and/or range inter-planar estimation of said one or more objects within said scene based on said determined intensity values.

2. The method according to claim 1, further comprising performing predefined computational operations based on the determined intensity values.

3. The method according to claim 2, wherein performing the predefined computational operations comprises correlating the intensity values to at least one predefined lookup table.

4. The method according to claim 3, further comprising determining two highest correlation values MAX₁ and MAX₂, said two highest correlation values being corresponding to the first and second planes of the scene, respectively.

5. The method according to claim 4, further comprising determining additional two highest correlation values MIN₁ and MIN₂, besides the determined MAX₁ and MAX₂ values, said two highest correlation values corresponding to the first and second planes of the scene, respectively.

6. The method according to claim 2, wherein the performing predefined computational operations comprises calculating a peak ratio of the first and second planes of the scene.

7. The method according to claim 4, further comprising calculating the peak ratio of the first and second planes of the scene by dividing the highest correlation value MAX₁ on the highest correlation value MAX₂.

8. The method according to claim 2, wherein performing predefined computational operations comprises calculating a Signal-to-Noise ratio SNR₁ at the first plane of the scene.

9. The method according to claim 5, further comprising calculating the Signal-to-Noise ratio SNR₁ at the first plane of the scene by dividing the highest correlation value MAX₁ on the MIN₁ value.

10. The method according to claim 2, wherein the performing predefined computational operations comprises calculating a Signal-to-Noise ratio SNR₂ at the second plane of the scene.

11. The method according to claim 5, further comprising calculating the Signal-to-Noise ratio SNR₂ at the second plane of the scene by dividing the highest correlation value MAX₂ on the MIN₁ value.

12. The method according to claim 2, further comprising determining a 3-D geometric shape of the one or more objects within the scene based on the predefined computational operations.

13. The method according to claim 1, further comprising providing the coded light pattern as one or more of the following:

a) a periodic pattern; and

b) a non-periodic pattern.

14. An apparatus configured to perform 3-D (three-dimensional) object profile inter-planar estimation and/or range inter-planar estimation of one or more objects within a scene, said apparatus comprising:

a) a predefined set of types of features, giving rise to feature types, each feature type being distinguishable according to a unique bi-dimensional formation;

b) a coded light pattern, having axially varying intensity, comprising multiple appearances of said feature types;

c) a projector configured to project said coded light pattern on one or more objects within a scene, said scene having at least two planes, giving rise to a first plane and to a second plane;

d) an imaging device configured to capture a 2-D (two-dimensional) image of said objects having said projected coded light pattern projected thereupon, giving rise to a 2-D captured image, said 2D image comprising reflected said feature types; and

e) a processing unit configured to;

e.1. determine intensity values of the 2-D captured image, giving rise to the determined intensity values; and

e.2. perform 3-D object profile inter-planar estimation and/or range inter-planar estimation of said one or more objects within said scene by using the determined intensity values.

15. The apparatus according to claim 14, further comprising a diffractive optical element designed to enable projecting said coded light pattern toward said one or more objects.

16. The apparatus according to claim 15, wherein the diffractive optical element is a beam splitter.

17. The apparatus according to claim 14, wherein the projector further comprises a phase mask configured to enable obtaining corresponding intensity values on the 2-D image to be captured.

18. The apparatus according to claim 17, wherein the phase mask is a beam shaping diffractive optical element.

19. The apparatus according to claim 14, wherein the processing unit is further configured to perform predefined computational operations based on the determined intensity values.

20. The apparatus according to claim 14, wherein the processing unit is further configured to correlate the intensity values to at least one predefined lookup table.

21. The apparatus according to claim 14, wherein the processing unit is further configured to determine two highest correlation values MAX₁ and MAX₂, said two highest correlation values corresponding to the first and second planes of the scene, respectively.

22. The apparatus according to claim 21, wherein the processing unit is further configured to determine additional two highest correlation values MIN₁ and MIN₂, besides the determined MAX₁ and MAX₂ values, said two highest correlation values being corresponding to the first and second planes of the scene, respectively.

23. The apparatus according to claim 14, wherein the processing unit is further configured to calculate a peak ratio of the first and second planes of the scene.

24. The apparatus according to claim 21, wherein the peak ratio of the first and second planes of the scene is calculated by dividing the highest correlation value MAX₁ by the highest correlation value MAX₂.

25. The apparatus according to claim 14, wherein the processing unit is further configured to calculate a Signal-to-Noise ratio SNR₁ at the first plane of the scene.

26. The apparatus according to claim 22, wherein the Signal-to-Noise ratio SNR₁ at the first plane of the scene is calculated by dividing the highest correlation value MAX₁ by the MIN₁ value.

27. The apparatus according to claim 14, wherein the processing unit is further configured to calculate a Signal-to-Noise ratio SNR₂ at the second plane of the scene.

28. The apparatus according to claim 22, wherein the Signal-to-Noise ratio SNR₂ at the second plane of the scene is calculated by dividing the highest correlation value MAX₂ by the MIN₁ value.

29. The apparatus according to claim 19, wherein the processing unit is further configured to determine a 3-D geometric shape of the one or more objects within the scene based on the predefined computational operations.

30. The apparatus according to claim 14, wherein the coded light pattern is one or more of the following:

a) a periodic pattern; and

b) a non-periodic pattern.

31. The apparatus according to claim 14, wherein the processing unit is provided within the imaging device.

32. A system configured to perform 3-D (three-dimensional) object profile inter-planar estimation and/or range inter-planar estimation of one or more objects within a scene, said system comprising:

a) a pattern-projecting apparatus configured to project a bi-dimensional coded light pattern, having axially varying intensity and having a plurality of feature types onto one or more objects within a scene having at least two planes, such that each feature reflects from a respective reflection location of the object within said scene, giving rise to a reflected feature, said feature types being distinguishable according to a unique bi-dimensional formation;

b) an imaging device configured to capture a 2-D (two-dimensional) image of said scene comprising the reflected features; and

c) a processing unit configured to;

c.1. determine intensity values of the 2-D captured image, giving rise to the determined intensity values; and

c.2. perform 3-D object profile inter-planar estimation and/or range inter-planar estimation of said one or more objects within said scene by making use of the determined intensity values.

33. The system according to claim 32, wherein the pattern-projecting apparatus further comprises a projector configured to project the coded light pattern on the one or more objects within the scene.

34. The system according to claim 32, further comprising a diffractive optical element designed to enable projecting said coded light pattern toward said one or more objects.

35. The system according to claim 34, wherein the diffractive optical element is a beam splitter.

36. The system according to claim 32, wherein the pattern-projecting apparatus further comprises a phase mask configured to enable obtaining corresponding intensity values on the 2-D image to be captured.

37. The system according to claim 36, wherein the phase mask is a beam shaping diffractive optical element.

38. The system according to claim 32, wherein the processing unit is further configured to perform predefined computational operations based on the determined intensity values.

39. The system according to claim 32, wherein the processing unit is further configured to correlate the intensity values to at least one predefined lookup table.

40. The system according to claim 32, wherein the processing unit is further configured to calculate a peak ratio of the first and second planes of the scene.

41. The system according to claim 32, wherein the processing unit is further configured to calculate a Signal-to-Noise ratio SNR₁ at the first plane of the scene.

42. The system according to claim 32, wherein the processing unit is further configured to calculate a Signal-to-Noise ratio SNR₂ at the second plane of the scene.

43. The system according to claim 38, wherein the processing unit is further configured to determine a 3-D geometric shape of the one or more objects within the scene based on the predefined computational operations.

44. The system according to claim 32, wherein the coded light pattern is one or more of the following:

a) a periodic pattern; and

b) a non-periodic pattern.

45. The system according to claim 32, wherein the processing unit is provided within the imaging device.

46. A method of generating a sequence of SOP (Single-Optical-Path) or nearly SOP patterns, said method comprising modulating a predefined pattern and using at least one of volumetric matrices, each of said volumetric matrices complying with one or more predefined rules.

47. The method according to claim 46, further comprising manipulating the sequence of patterns by using image processing.

48. The method according to claim 46, further comprising providing the volumetric matrices as one or more of the following:

a) a repeating matrices; and

b) a non-repeating matrices.

49. A method of generating a sequence of SOP (Single-Optical-Path) or nearly SOP patterns, said method comprising:

a) defining code formations and corresponding code formation changes;

b) generating one or more sets of local-uniqueness matrices, containing only said code formations;

c) generating at least one set of matrices containing only said local-uniqueness matrices;

d) generating at least one set of consecutive matrices, containing only sequences of said matrices, according to said code formation changes, while said at least one set of the consecutive matrices does not contain any axial repetitions of both local-uniqueness matrices and averaged local-uniqueness matrices;

e) generating at least one set of volumetric matrices, while said volumetric matrices containing only said consecutive matrices and not containing any axial repetitions of both local-uniqueness matrices and averaged local-uniqueness matrices; and

f) generating a sequence of patterns by modulating a predefined pattern and using at least one of said repeating volumetric matrices.

50. The method according to claim 49, further comprising manipulating the sequence of patterns by using image processing.

51. The method according to claim 49, further comprising providing the matrices with symmetries other than translational.

52. The method according to claim 49, further comprising generating, for obtaining the sequence of nearly SOP patterns, the at least one set of the matrices that comprises repetitions of the local-uniqueness matrices in a predefined direction or do not comprise said repetitions.

53. The method according to claim 49, further comprising generating, for obtaining the sequence of nearly SOP patterns, the at least one set of the consecutive matrices that comprises repetitions of the both local-uniqueness matrices and averaged local-uniqueness matrices in a predefined direction or do not comprise said repetitions.

54. The method according to claim 49, further comprising generating, for obtaining the sequence of nearly SOP patterns, the at least one set of volumetric matrices that comprises repetitions of the both local-uniqueness matrices and averaged local-uniqueness matrices in a predefined direction or do not comprise said repetitions.