System and related methods for automatically aligning 2D images of a scene to a 3D model of the scene

• US 20080310757A1
• Filed: 06/11/2008
• Published: 12/18/2008
• Est. Priority Date: 06/15/2007
• Status: Abandoned Application

• Current Assignee: Brainstorm Technology, LLC
• Original Assignee: Brainstorm Technology, LLC
• Inventors: Wolberg, George, Liu, Lingyun, Zokai, Siavash, Yu, Gene, Stamos, Ioannis
• Application Number:
  US12/157,595
• Publication Number:
  US 20080310757A1
• Time in Patent Office: Days
• US Class Current: 382/285
• CPC Class Codes: G06V 20/647 by matching two-dimensional images to three-dimensional objects
• CPC Class Codes: G06V 20/653 by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces

Abstract

A system and related method for automatically aligning a plurality of 2D images of a scene to a first 3D model of the scene. The method includes providing a plurality of 2D images of the scene, generating a second 3D model of the scene based on the plurality of 2D images, generating a transformation between the second 3D model and the first 3D model based on a comparison of at least one of the plurality of 2D images to the first 3D model, and using the transformation to automatically align the plurality of 2D images to the first 3D model.

Claims

1. A method for automatically aligning a plurality of 2D images of a scene to a first 3D model of the scene, the method comprising:

a. providing a plurality of 2D images of the scene;

b. generating a second 3D model of the scene based on the plurality of 2D images;

c. generating a transformation between the second 3D model and the first 3D model based on a comparison of at least one of the plurality of 2D images to the first 3D model; and

d. using the transformation to automatically align the plurality of 2D images to the first 3D model.

2. The method according to claim 1, wherein the step of generating a second 3D model based on the plurality of 2D images includes generating a sparse 3D point cloud from the plurality of 2D images using a multiview geometry algorithm.

3. The method according to claim 1, where the first 3D model is generated from a range scan.

4. The method according to claim 1, where the first 3D model is received from a 3D computer modeling software tool.

5. The method according to claim 2, wherein:

a. the scene includes an object;

b. the object includes a plurality of features;

c. each of the plurality of features has one of a plurality of 3D positions;

d. the plurality of 2D images were created using a 2D sensor;

e. the 2D sensor was at one of a plurality of sensor positions relative to the image when each of the plurality of 2D images was created; and

f. the multiview geometry algorithm is used to determine at least one of the plurality of sensor positions and at least one of the plurality of 3D positions.

6. The method according to claim 2, wherein:

a. the plurality of 2D images are mathematically represented as a sequence of N images, I={I₁, I₂, . . . , I_N}, wherein the i^th image in the sequence is denoted I_i;

b. the plurality of 2D images include 2D features;

b. the 2D images were generated using a 2D sensor having a lens;

c. the lens is characterized as having a lens distortion; and

d. the multiview geometry algorithm includes the following steps;

i. determining the lens distortion,

ii. compensating for the lens distortion in the sequence of N images representing the plurality of 2D images, {I₁, I₂, . . . , I_N},

iii. for each pair of successive 2D images, I_i and I_i+1, generating a list of 2D features matches using a feature-based matching process,

iv. computing an initial motion and an initial structure from first two 2D images in the sequence, I₁ and I₂, and

v. computing a motion and a structure for pairs of successive 2D images, I<sub>i−

1</sub> and I_i, for each value i in the range from 3 to N.

7. The method according to claim 6, wherein the initial motion and the initial structure from 2D images I₁ and I₂ are computed as follows:

a. calculating a relative pose of the 2D sensor that includes a rotation transformation R and a translation vector T by decomposing an essential matrix E=K^TFK, wherein the matrix K includes internal calibration parameters for the 2D sensor and F is a fundamental matrix;

b. setting a pose of the 2D sensor for the first 2D image I₁ where R₁ is an identity matrix, and T₁ is an all-zero vector;

c. setting a pose of the 2D sensor for the second 2D image I₂ so R₂=R, and T₂=T;

d. computing an initial point cloud of 3D points X_j from 2D correspondences between I₁ and I₂ though triangulation; and

e. refining the relative pose of the 2D sensor by minimizing a geometric reprojection error.

8. The method according to claim 6, wherein the multiview geometry algorithm further includes the following steps to process image I_i for each value i in the range from 3 to N:

a. determining a set of common features between the three images I<sub>i−

2</sub>, I<sub>i−

1</sub>, and I_i, where the common features are the features that have been tracked from frame I<sub>i−

2</sub> to frame I<sub>i−

1</sub> and then to frame I_i via the feature-based matching process;

b. recording 3D points that are associated with the matched features between I<sub>i−

2</sub> and I<sub>i−

1</sub>;

c. computing the pose (R_i, T_i) of the image I_i from the 2D features and the 3D points using a Direct Linear Transform (“

DLT”

) with a Random Sample Consensus (“

RANSAC”

) for outlier detection;

d. refining the pose using a nonlinear steepest-descent algorithm,

e. computing from the remaining 2D features that are seen in images I<sub>i−

1</sub> and I_i and not seen in image I<sub>i−

2</sub> a new set of 3D points X′

_j;

f. projecting the new set of 3D points onto the previous images of the sequence I<sub>i−

2</sub>, . . . , I_i in order to reinforce more correspondence between sub-sequences of the images in the list; and

g. adding new corresponding features and 3D points X′

_j to the database of feature correspondences and 3D points.

9. The method according to claim 8, wherein the multiview geometry algorithm further includes performing a global bundle adjustment procedure that involves all of the 2D images from the sequence by minimizing a reprojection error.

10. The method according to claim 1, wherein:

a. each of the plurality of 2D images was collected from one of a plurality of viewpoints; and

b. no advance knowledge of the plurality of viewpoints is required before performing the method according to claim 1 if at least one of the plurality of 2D images overlaps the 3D model.

11. The method according to claim 1, wherein the step of generating the transformation between the second 3D model and the first 3D model comprises the steps of:

forming hypotheses by randomly selecting matches among the first 3D model and second 3D model;

testing these hypotheses on all of the matches between the first 3D model and second 3D model; and

selecting a scale factor that is most consistent with the complete dataset.

12. A method for texture mapping a plurality of 2D images of a scene to a 3D model of the scene, the method comprising:

a. providing a plurality of 3D range scans of the scene;

b. generating a first 3D model of the scene based on the plurality of 3D range scans;

c. providing a plurality of 2D images of the scene;

d. generating a second 3D model of the scene based on the plurality of 2D images;

e. registering at least one of the plurality of 2D images with the first 3D model;

f. generating a transformation between the second 3D model and the first 3D model as a result of registering the at least one of the plurality of 2D images with the first 3D model; and

g. using the transformation to automatically align the plurality of 2D images to the first 3D model.

13. The method according to claim 12, wherein:

a. the plurality of 3D range scans include lines; and

b. the step of generating the first 3D model based on the plurality of 3D range scans includes generating a dense 3D point cloud using a 3D-to-3D registration method that;

i. matches the lines in the plurality of 3D range scans, and

ii. brings the plurality of 3D range scans into a common reference frame.

14. The method according to claim 12, wherein the step of generating the second 3D model based on the plurality of 2D images includes generating a sparse 3D point cloud from the plurality of 2D images using a multiview geometry algorithm.

15. The method according to claim 14, wherein:

a. the scene includes an object;

b. the object includes a plurality of features;

c. each of the plurality of features has one of a plurality of 3D positions;

d. the plurality of 2D images were created using a 2D sensor;

e. the 2D sensor was at one of a plurality of sensor positions relative to the image when each of the plurality of 2D images was created; and

f. the multiview geometry algorithm is used to determine at least one of the plurality of sensor positions and at least one of the plurality of 3D positions.

16. The method according to claim 12, wherein:

a. the plurality of 3D range scans are collected from a first plurality of viewpoints;

b. the plurality of 2D images are collected from a second plurality of viewpoints; and

c. not all of the second plurality of viewpoints coincide with the first plurality of viewpoints.

17. The method according to claim 12, wherein:

a. each of the plurality of 2D images is collected from one of a plurality of viewpoints; and

b. no advance knowledge of the plurality of viewpoints is required before performing the method if at least one of the plurality of 2D images overlaps the 3D model.

18. The method according to claim 12, wherein the step of generating the transformation between the second 3D model and the first 3D model comprises the steps of:

forming hypotheses by randomly selecting matches among the first 3D model and second 3D model;

testing these hypotheses on all of the matches between the first 3D model and second 3D model; and

selecting a scale factor that is most consistent with the complete dataset.

19. A system comprising:

a 3D sensor configured to generate a plurality of 3D range scans of a scene;

a 2D sensor configured to generate a plurality of 2D images of the scene; and

a computer that is coupled to the 3D sensor and the 2D sensor, and includes a computer-readable medium having a computer program that, when executed by the computer, texture maps the plurality of 2D images of the scene onto a first 3D model of the scene, wherein the computer is operable to do the following steps;

i. receive as input the plurality of 3D range scans and the plurality of 2D images,

ii. generate the first 3D model of the scene based on the plurality of 3D range scans,

iii. generate a second 3D model of the scene based on the plurality of 2D images,

iv. register at least one of the plurality of 2D images with the first 3D model,

v. generate a transformation between the second 3D model and the first 3D model as a result of the registering of the at least one of the plurality of 2D images with the first 3D model, and

vi. use the transformation to automatically align the plurality of 2D images to the first 3D model.

20. The system according to claim 19, wherein:

a. the 3D sensor is configured to generate the plurality of 3D range scans of the scene from a first plurality of viewpoints;

b. the 2D sensor is configured to generate the plurality of 2D images of the scene from a second plurality of viewpoints; and

c. not all of the second plurality of viewpoints coincide with the first plurality of viewpoints.