Geometry encoder

Patent metadata

Field	Value
Publication number	US-10891758-B2
Application number	US-201816042738-A
Country	US
Kind code	B2
Filing date	Jul 23, 2018
Priority date	Jul 23, 2018
Publication date	Jan 12, 2021
Grant date	Jan 12, 2021

Abstract

A method includes receiving geometric data to be encoded, generating a signature for the geometric data based on the at least one property associated with the geometric data, enumerating a set of first options, enumerating a set of second options, encoding the geometric data using the enumerated first option and the enumerated second option, decoding the encoded geometric data, selecting one of the enumerated second options based on a cost function, and training a classifier based on the signature, the enumerated first option and the selected second option.

First claim

What is claimed is:

1. A method comprising:
receiving geometric data to be encoded;
generating a signature for the geometric data based on at least one property associated with the geometric data, the signature being represented by a number of variables based on a statistical analysis of the at least one property;
changing at least one variable value for a first set of parameters associated with compressing the geometric data to define a first set of options;
changing at least one variable value for a second set of parameters associated with compressing the geometric data to define a set of second options;
encoding the geometric data using the first set of options and the set of second options;
decoding the encoded geometric data;
selecting one of the set of second options based on a cost function, the cost function being based on a feedback from at least one of a compression of the geometric data or a decompression of the geometric data; and
training a classifier based on the signature, the first set of options and the selected second option.

2. The method of claim 1 , wherein
the geometric data is mesh data,
the at least one property includes at least one of a number of vertices, a number of edges, and a number of triangles, and
the signature is based on the number of vertices, the number of edges, and the number of triangles in the mesh data.

3. The method of claim 1 , wherein
the geometric data is mesh data,
the at least one property includes a number of connected components for at least one attribute, and
the signature is based on the number of connected components for the at least one attribute in the mesh data.

4. The method of claim 1 , wherein
the geometric data is mesh data,
the at least one property includes a number of boundary edges for at least one attribute, and
the signature is based on the number of boundary edges for the at least one attribute in the mesh data.

5. The method of claim 1 , wherein
the geometric data is mesh data,
the at least one property includes an angle of a triangles corner, and
the signature is based on a statistical analysis of a histogram of the angles of triangle corners in the mesh data.

6. The method of claim 1 , wherein
the geometric data is mesh data,
the at least one property includes angles between triangles, and
the signature is based on a statistical analysis of a histogram of the angles between triangles in the mesh data.

7. The method of claim 1 , wherein
the geometric data is mesh data,
the at least one property includes vertex valences, and
the signature is based on a statistical analysis of a histogram of the vertex valences in the mesh data.

8. The method of claim 1 , wherein the first set of options includes a fixed option and an environmental option.

9. The method of claim 1 , wherein the cost function is based on a performance associated with encoding the geometric data and a performance associated with decoding the encoded geometric data.

10. The method of claim 1 , wherein
the statistical analysis of the at least one property includes a histogram including a plurality of numerical ranges, and
at least a portion of the number of variables correspond to the plurality of numeric ranges.

11. A method comprising:
receiving geometric data to be encoded;
generating a signature for the geometric data based on at least one property associated with the geometric data, the signature being represented by a number of variables based on a statistical analysis of the at least one property;
receiving a first set of options defined by a first set of parameters associated with compressing the geometric data;
accessing a classifier based on the signature and the first set of options;
selecting a set of second options based on the classifier, the set of second options being defined by a second set of parameters associated with compressing the geometric data; and
encoding the geometric data using the first set of options and the set of second options.

12. The method of claim 11 , wherein
the geometric data is mesh data,
the signature is based on at least one of a number of vertices, a number of edges, and a number of triangles in the mesh data, and
accessing the classifier uses a trained machine learning model based on one of a random forest model, a neural network model and a cluster analysis model.

13. The method of claim 11 , wherein
the geometric data is mesh data,
the signature is based on a number of connected components for at least one attribute in the mesh data, and
accessing the classifier uses a trained machine learning model based on one of a random forest model, a neural network model and a cluster analysis model.

14. The method of claim 11 , wherein
the geometric data is mesh data,
the signature is based on a number of boundary edges for at least one attribute in the mesh data, and
accessing the classifier uses a trained machine learning model based on one of a random forest model, a neural network model and a cluster analysis model.

15. The method of claim 11 , wherein
the geometric data is mesh data,
the signature is based on a histogram of an angle of a triangles corner in the mesh data, and
accessing the classifier uses a trained machine learning model based on one of a random forest model, a neural network model and a cluster analysis model.

16. The method of claim 11 , wherein
the geometric data is mesh data,
the signature is based on a histogram of angles between triangles in the mesh data, and
accessing the classifier uses a trained machine learning model based on one of a random forest model, a neural network model and a cluster analysis model.

17. The method of claim 11 , wherein
the geometric data is mesh data,
the signature is based on a histogram of vertex valences in the mesh data, and
accessing the classifier uses a trained machine learning model based on one of a random forest model, a neural network model and a cluster analysis model.

18. The method of claim 11 , wherein the first set of options include a fixed option and an environmental option.

19. The method of claim 11 , wherein
the first set of options include a fixed option and an environmental option,
the selecting of the second option is based on the fixed option and the environmental option, and
the encoding of the geometric data uses the fixed option.

20. A non-transitory computer-readable storage medium having stored thereon computer executable program code which, when executed on a computer system, causes the computer system to perform steps comprising:
receiving geometric data to be encoded;
generating a signature for the geometric data based on at least one property associated with the geometric data, the signature being represented by a number of variables based on a statistical analysis of the at least one property;
receiving a first set of options defined by a first set of parameters associated with compressing the geometric data;
accessing a classifier based on the signature and the first set of options;
selecting a set of second options based on the classifier, the set of second options being defined by a second set of parameters associated with compressing the geometric data; and
encoding the geometric data using the first set of options and the set of second options.

Assignees

• Google Llc

Inventors

• Hemmer Michael

Classifications

• G06T9/002 Primary

  using neural networks · CPC title

• G06T9/001 Primary

  Model-based coding, e.g. wire frame · CPC title

• G06V20/64

  Three-dimensional [3D] objects · CPC title

• G06V10/50

  by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis · CPC title

• G06F18/24

  Classification techniques · CPC title