Geometry encoder

Patent metadata

Field	Value
Publication number	US-2020027246-A1
Application number	US-201816042738-A
Country	US
Kind code	A1
Filing date	Jul 23, 2018
Priority date	Jul 23, 2018
Publication date	Jan 23, 2020
Grant date	—

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 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.

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 enumerating the second set of options includes enumerating all of the second set of options.

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

10 . 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.

11 . A method comprising:
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; receiving a first set of options; accessing a classifier based on the signature and the first set of options; selecting a set of second options based on the classifier; and encoding the geometric data using the first set of options and the second set of 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 the at least one property associated with the geometric data; receiving a first set of options; accessing a classifier based on the signature and the first set of options; selecting a set of second options based on the classifier; and encoding the geometric data using the first set of options and the second set of 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