SHAPED-BASED TECHNIQUES FOR EXPLORING DESIGN SPACES

• US 20200134909A1
• Filed: 10/29/2018
• Published: 04/30/2020
• Est. Priority Date: 10/29/2018
• Status: Active Grant

• Current Assignee: Autodesk, Inc.
• Original Assignee: Autodesk, Inc.
• Inventors: DAVIES, Thomas, HALEY, Michael, DANIELYAN, Ara, FABIAN, Morgan
• Granted Patent: US 11,741,662 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 2111/20 Configuration CAD, e.g. designing by assembling or positioning modules selected from libraries of predesigned modules
• CPC Class Codes: G06F 30/10 Geometric CAD
• CPC Class Codes: G06F 30/12 characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
• CPC Class Codes: G06F 30/27 using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 15/20 Perspective computation
• CPC Class Codes: G06T 2200/24 involving graphical user interfaces [GUIs]
• CPC Class Codes: G06V 10/76 based on eigen-space representations, e.g. from pose or different illumination conditions; Shape manifolds
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/64 Three-dimensional objects

Abstract

In various embodiments, a training application generates a trained encoder that automatically generates shape embeddings having a first size and representing three-dimensional (3D) geometry shapes. First, the training application generates a different view activation for each of multiple views associated with a first 3D geometry based on a first convolutional neural network (CNN) block. The training application then aggregates the view activations to generate a tiled activation. Subsequently, the training application generates a first shape embedding having the first size based on the tiled activation and a second CNN block. The training application then generates multiple re-constructed views based on the first shape embedding. The training application performs training operation(s) on at least one of the first CNN block and the second CNN block based on the views and the re-constructed views to generate the trained encoder.

Claims

1. A computer-implemented method for automatically exploring design spaces, the method comprising:

for each design included in a plurality of designs, computing a different set of views based on a three-dimensional (3D) geometry associated with the design;

for each set of views, computing a different shape embedding based on a trained encoder;

in response to receiving a command via a graphical user interface (GUI), determining one or more target designs included in the first plurality of designs based on at least a first shape embedding; and

displaying the one or more target designs via the GUI, wherein the target designs reflect a preference associated with the first command.

2. The computer-implemented method of claim 1, wherein the plurality of designs is generated via a generative design flow.

3. The computer-implemented method of claim 1, wherein determining the one or more target designs comprises:

computing a distance between a first shape embedding and a second shape embedding;

determining that a first design included in the plurality of designs is similar to a second design included in the plurality of designs based on the distance; and

adding the first design to a list of designs that are similar to the second design, wherein the list includes the one or more target designs.

4. The computer-implemented method of claim 1, wherein determining the one or more target designs comprises:

performing one or more comparison operations between two or more of the shape embeddings to generate a ranked list of shape embeddings; and

generating a ranked list of designs based on the ranked list of shape embeddings, wherein the ranked list includes the one or more target designs.

5. The computer-implemented method of claim 1, wherein determining the one or more target designs comprises:

performing multiple pairwise comparison operations between the shape embeddings to generate a plurality of distances;

generating a histogram based on the plurality of distances;

determining a similarity threshold based on the histogram; and

determining the target designs from the plurality of designs based on the plurality of distances and the similarity threshold.

6. The computer-implemented method of claim 1, wherein determining the one or more target designs comprises performing one or more clustering operations on the plurality of designs based on the shape embeddings to generate a first shape cluster that includes the one or more target designs.

7. The computer-implemented method of claim 1, wherein determining the one or more target designs comprises:

computing a plurality of distances based on the shape embeddings; and

performing one or more filtering operations on the plurality of designs based on the plurality of distances to determine the one or more target designs.

8. The computer-implemented method of claim 1, wherein determining the one or more target designs comprises searching the plurality of designs based on the shape embeddings to determine at least one design included in the plurality of designs.

9. The computer-implemented method of claim 1, wherein each view included in each set of views comprises a different rendering.

10. The computer-implemented method of claim 1, wherein the trained encoder comprises at least one convolutional neural network (CNN).

11. One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:

for each design included in a plurality of designs, computing a different set of views based on a three-dimensional (3D) geometry associated with the design;

for each set of views, computing a different shape embedding based on a trained encoder;

in response to receiving a command via a graphical user interface (GUI), determining one or more target designs included in the first plurality of designs based on at least a first shape embedding; and

displaying the one or more target designs via the GUI, wherein the target designs reflect a preference associated with the first command.

12. The one or more non-transitory computer readable media of claim 11, wherein the plurality of designs is generated via a generative design flow.

13. The one or more non-transitory computer readable media of claim 11, wherein determining the one or more target designs comprises:

computing a distance between a first shape embedding and a second shape embedding;

determining that a first design included in the plurality of designs is dissimilar to a second design included in the plurality of designs based on the distance; and

adding the first design to a list of designs that are dissimilar to the second design, wherein the list includes the one or more target designs.

14. The one or more non-transitory computer readable media of claim 11, wherein determining the one or more target designs comprises:

performing one or more comparison operations between two or more of the shape embeddings to generate a ranked list of shape embeddings; and

generating a ranked list of designs based on the ranked list of shape embeddings, wherein the ranked list includes the one or more target designs.

15. The one or more non-transitory computer readable media of claim 11, wherein determining the one or more target designs comprises:

performing multiple pairwise comparison operations between the shape embeddings to generate a plurality of distances; and

determining the target designs from the plurality of designs based on the plurality of distances and a similarity threshold.

16. The one or more non-transitory computer readable media of claim 11, wherein determining the one or more target designs comprises performing one or more clustering operations on the plurality of designs based on the shape embeddings to generate a first shape cluster that includes the one or more target designs.

17. The one or more non-transitory computer readable media of claim 11, wherein determining the one or more target designs comprises:

computing a plurality of distances based on the shape embeddings; and

performing one or more filtering operations on the plurality of designs based on the plurality of distances to determine the one or more target designs.

18. The one or more non-transitory computer readable media of claim 11, wherein determining the one or more target designs comprises searching the plurality of designs based on the shape embeddings to determine at least one design included in the plurality of designs.

19. The one or more non-transitory computer readable media of claim 11, wherein computing a set of views based on the 3D geometry associated with the design comprises, for each virtual camera included in a plurality of virtual cameras, rendering the 3D geometry based on the virtual camera to generate a different view included in the set of views.

20. A system, comprising:

one or more memories storing instructions; and

one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to;

for each design included in a plurality of designs, compute a different set of views based on a three-dimensional (3D) geometry associated with the design;

for each set of views, compute a different shape embedding based on a trained encoder;

in response to receiving a command via a graphical user interface (GUI), determine one or more target designs included in the first plurality of designs based on at least a first shape embedding; and

display the one or more target designs via the GUI, wherein the target designs reflect a preference associated with the first command.