System and method for 3D blob classification and transmission

Patent metadata

Field	Value
Publication number	US-10762394-B2
Application number	US-201816050998-A
Country	US
Kind code	B2
Filing date	Jul 31, 2018
Priority date	Jul 31, 2018
Publication date	Sep 1, 2020
Grant date	Sep 1, 2020

Abstract

Embodiments described herein provide an apparatus comprising a processor to project and accumulate three-dimensional (3D) point data from a blob onto one of a floor plane or a ceiling plane, construct a histogram of the 3D point data, determine a center of mass of the point data from the blob, obtain height data for the 3D point data, and calculate dimensions for a bounding box to surround the blob. Other embodiments may be described and claimed.

First claim

What is claimed is:

1. A method, comprising:
projecting and accumulating three-dimensional (3D) point data from a blob onto one of a floor plane or a ceiling plane;
constructing a histogram of the 3D point data;
determining a center of mass of the blob from the histogram of the 3D point data using a two-dimensional (2D) distance transform;
obtaining height data for the 3D point data; and
calculating dimensions for a bounding box to surround the blob.

2. The method of claim 1 , further comprising:
encoding the bounding box with coordinates for two opposing corners of the bounding box.

3. The method of claim 1 , further comprising:
implementing a classification algorithm to identify one or more blobs in a point cloud data set.

4. The method of claim 3 , wherein the classification algorithm comprises a K-means classification algorithm.

5. The method of claim 1 , further comprising:
calculating the 2D distance transform for the blob to generate peaks in coordinates for data in the blob; and
surrounding the peaks with a 2D rectangle defined by a diameter of the blob.

6. The method of claim 5 , further comprising:
merging the 2D rectangle with the height data to define the bounding box.

7. A non-transitory machine readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:
projecting and accumulating three-dimensional (3D) point data from a blob onto one of a floor plane or a ceiling plane;
constructing a histogram of the 3D point data;
determining a center of mass of the blob from the histogram of the 3D point data using a two-dimensional (2D) distance transform;
obtaining height data for the 3D point data; and
calculating dimensions for a bounding box to surround the blob.

8. The non-transitory machine readable medium of claim 7 , the operations additionally comprising:
encoding the bounding box with coordinates for two opposing corners of the bounding box.

9. The non-transitory machine readable medium of claim 7 , the operations additionally comprising:
implementing a classification algorithm to identify one or more blobs in a point cloud data set.

10. The non-transitory machine readable medium of claim 9 , wherein the classification algorithm comprises a K-means classification algorithm.

11. The non-transitory machine readable medium of claim 7 , the operations additionally comprising:
calculating the 2D distance transform for the blob to generate peaks in coordinates for data in the blob; and
surrounding the peaks with a 2D rectangle defined by a diameter of the blob.

12. The non-transitory machine readable medium of claim 11 , the operations additionally comprising:
merging the 2D rectangle with the height data to define the bounding box.

13. An apparatus, comprising:
a processor to project and accumulate three-dimensional (3D) point data from a blob onto one of a floor plane or a ceiling plane, construct a histogram of the 3D point data, determine a center of mass of the blob from the histogram of the 3D point data using a two-dimensional (2D) distance transform, obtain height data for the 3D point data, and calculate dimensions for a bounding box to surround the blob; and
a memory communicatively coupled to the processor.

14. The apparatus of claim 13 , the processor to encode the bounding box with coordinates for two opposing corners of the bounding box.

15. The apparatus of claim 13 , the processor to implement a classification algorithm to identify one or more blobs in a point cloud data set.

16. The apparatus of claim 15 , wherein the classification algorithm comprises a K-means classification algorithm.

17. The apparatus of claim 13 , the processor to calculate the 2D distance transform for the blob to generate peaks in coordinates for data in the blob, and surround the peaks with a 2D rectangle defined by a diameter of the blob.

18. The apparatus of claim 17 , the processor to merge the 2D rectangle with the height data to define the bounding box.

Assignees

• Intel Corp

Inventors

• Distler Jonathan
• Ashkenazi Tzach
• Kaufman Itay
• Bortman Maria

Classifications

• G06V20/653

  by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces · CPC title

• G06V30/422

  Technical drawings; Geographical maps · CPC title

• G06T7/11 Primary

  Region-based segmentation · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06F18/23213

  with fixed number of clusters, e.g. K-means clustering · CPC title