SYSTEMS AND METHODS FOR GENERATING DATA INDICATIVE OF A THREE-DIMENSIONAL REPRESENTATION OF A SCENE

• US 20160189419A1
• Filed: 08/08/2014
• Published: 06/30/2016
• Est. Priority Date: 08/09/2013
• Status: Abandoned Application

• Current Assignee: Sweep3d Corporation
• Original Assignee: Sweep3d Corporation
• Inventors: FAKIH, Adel, ZELEK, John
• Application Number:
  US14/911,152
• Publication Number:
  US 20160189419A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01B 11/24 for measuring contours or curvatures
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 15/20 Perspective computation
• CPC Class Codes: G06T 17/005 Tree description, e.g. octree, quadtree
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2200/08 involving all processing steps from image acquisition to 3D model generation
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/20072 Graph-based image processing
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20164 Salient point detection; Corner detection
• CPC Class Codes: G06T 2207/30244 Camera pose
• CPC Class Codes: G06T 2215/16 Using real world measurements to influence rendering
• CPC Class Codes: G06T 7/536 from perspective effects, e.g. by using vanishing points
• CPC Class Codes: G06T 7/55 from multiple images

Abstract

According to one aspect, there are systems and methods for generating data indicative of a three-dimensional representation of a scene. Current depth data indicative of a scene is generated using a sensor. Salient features are detected within a depth frame associated with the depth data, and these salient features are matched with a saliency likelihoods distribution. The saliency likelihoods distribution represents the scene, and is generated from previously-detected salient features. The pose of the sensor is estimated based upon the matching of detected salient features, and this estimated pose is refined based upon a volumetric representation of the scene. The volumetric representation of the scene is updated based upon the current depth data and estimated pose. A saliency likelihoods distribution representation is updated based on the salient features. Image data indicative of the scene may also be generated and used along with depth data.

Claims

1. A computer-implemented method for generating three-dimensional (“

3D”

) data, the method comprising;

(a) generating depth data indicative of a scene using a sensor, the depth data being associated with a current depth frame;

(b) detecting salient features within the current depth frame based upon the depth data;

(c) matching the detected salient features for the current depth frame with a saliency likelihoods distribution representation of the scene generated from previously detected salient features for a previously generated depth frame;

(d) determining an estimated pose of the sensor based upon the matching of detected salient features;

(e) refining the estimated pose based upon a volumetric representation of the scene; and

(f) updating the volumetric representation of the scene based on the current depth data and the refined estimated pose and updating the saliency likelihoods distribution representation based on the salient features for the current depth frame and the refined estimated pose.

2. The method of claim 1, wherein matching the detected salient features for the current frame with previously detected salient features for a previously generated frame comprises:

(a) obtaining previously estimated position and direction of the at least one sensor associated with the previously recorded salient features;

(b) determining uncertainty area based upon the previously estimated position and direction of the at least one sensor, the uncertainty area being indicative the estimated position and direction of the at least one sensor;

(c) identifying candidate features from the previously recorded salient features based upon whether these features can be detected if the at least one sensor is within the uncertainty area;

(d) comparing the candidates features to the detected salient features; and

(e) determining the estimated position and direction of the at least one sensor based upon the candidate features that match the detected features above a match threshold.

3. The method of claim 2, further comprising:

(a) determining saliency likelihood values for discrete spaces within a frame;

(b) generating descriptors for spaces that have the saliency likelihood values above a specified threshold; and

(c) storing the descriptors for use as the candidate features.

4. The method of claim 3, wherein at least one of the saliency likelihood values and the descriptors are stored based upon an oct-tree like data structure.

5. The method of claim 3, wherein the candidate features are identified based upon local maxima of the saliency likelihood values.

6. The method of claim 5, wherein the descriptor is a Histogram based descriptor.

7. The method of claim 1, wherein:

step (a) further comprises generating image data indicative of the scene using the sensor, the image data being associated with a current image frame;

step (b) further comprises detecting salient features for the image data within the current image frame based upon the image data; and

,

step (c) further comprises matching the detected salient features for the image data with the previously detected salient features for the image data.

8. The method of claim 7, wherein the salient features from the image data is detected using FAST algorithm.

9. The method of claim 7, wherein the descriptors for the salient features from the image data is generated using SURF algorithm.

10. The method of claim 6, wherein the salient features from the depth data is detected using NARF algorithm.

11. The method of claim 6, wherein the descriptors for the salient features from the depth data is generated using PFH algorithm.

12. The method of claim 7, wherein the depth data and image data is recorded by merging with the depth data and image data with previously recorded depth data and image data.

13. The method of claim 12, wherein at least one of the depth data is merged with at least one of the previously recorded depth data and image data using the equation:

14. A system for generating three-dimensional (“

3D”

) data, the system comprising;

(a) at least one sensor for generating depth data indicative of a scene;

(b) a processor operatively coupled to the at least one sensor, the processor configured for;

(i) generating depth data indicative of a scene using a sensor, the depth data being associated with a current depth frame;

(ii) detecting salient features within the current depth frame based upon the depth data;

(iii) matching the detected salient features for the current depth frame with a saliency likelihoods distribution representation of the scene generated from previously detected salient features for a previously generated depth frame;

(iv) determining an estimated pose of the sensor based upon the matching of detected salient features;

(v) refining the estimated pose based upon a volumetric representation of the scene; and

(vi) updating the volumetric representation of the scene based on the current depth data and the refined estimated pose and updating the saliency likelihoods distribution representation based on the salient features for the current depth frame and the refined estimated pose.

15. The system of claim 14, wherein the processor is further configured to match the detected salient features for the current frame with previously detected salient features for a previously generated frame by:

(a) obtaining previously estimated position and direction of the at least one sensor associated with the previously recorded salient features;

(b) determining uncertainty area based upon the previously estimated position and direction of the at least one sensor, the uncertainty area being indicative the estimated position and direction of the at least one sensor;

(c) identifying candidate features from the previously recorded salient features based upon whether these features can be detected if the at least one sensor is within the uncertainty area;

(d) comparing the candidates features to the detected salient features; and

(e) determining the estimated position and direction of the at least one sensor based upon the candidate features that match the detected features above a match threshold.

16. The system of claim 15, wherein the processor is further configured for:

(a) determining saliency likelihood values for discrete spaces within a frame;

(b) generating descriptors for spaces that have the saliency likelihood values above a specified threshold; and

(c) storing the descriptors for use as the candidate features.

17. The system of claim 14, wherein the at least one sensor is a handheld portable 3D sensor.

18. The system of claim 17, wherein the at least one sensor is a Kinect™

sensor.

19. The system of claim 14, wherein the processor comprises a graphics processing unit.

20. The system of claim 14, wherein the at least one sensor is a handheld sensor and the at least one processor is a processor in a mobile computing device.

21. A computer-implemented method for generating three-dimensional (“

3D”

) data, the method comprising;

(a) generating current depth data and current image data indicative of a scene using at least one sensor;

generating a current depth saliency map and current depth descriptors based upon the current depth data, and generating a current image saliency map and current image descriptors based upon the current image data;

(b) determining a current estimated pose of the at least one sensor based on aligning the current saliency maps with a scene saliency likelihoods representation, and aligning the current depth and image data with a scene surface representation;

(c) updating the scene surface representation based on the current depth data, the current image data, and the current estimated pose; and

,

(d) updating the scene saliency likelihoods representation based on the current saliency maps and the current estimated pose.