SHAPED-BASED TECHNIQUES FOR EXPLORING DESIGN SPACES

• US 20200134908A1
• 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 11,380,045 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: G06F 30/10 Geometric 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: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06T 15/20 Perspective computation

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 generating computational representations for three-dimensional (3D) geometry shapes, the method comprising:

for each view included in a first plurality of views associated with a first 3D geometry, generating a view activation based on a first convolutional neural network (CNN) block;

aggregating the view activations to generate a first tiled activation;

generating a first shape embedding having a fixed size based on the first tiled activation and a second CNN block;

generating a first plurality of re-constructed views based on the first shape embedding;

performing one or more training operations on at least one of the first CNN block or the second CNN block based on the first plurality of views and the first plurality of re-constructed views to generate a trained encoder; and

generating a second shape embedding having the fixed size based on the trained encoder.

2. The method of claim 1, wherein aggregating the view activations comprises concatenating the view activations along a single dimension.

3. The method of claim 1, wherein the second CNN block includes one or more 2D CNNs followed by a fully connected layer.

4. The method of claim 1, wherein performing the one or more training operations comprises:

determining that the first CNN block is not trained based on a loss function;

modifying one or more machine learning parameters associated with the first CNN block based on the loss function to generate a third CNN block;

generating a second plurality of re-constructed views based on the third CNN block and the second CNN block; and

determining that the third CNN block and the second CNN block comprise the trained encoder based on the loss function;

the first plurality of views; and

the second plurality of re-constructed views.

5. The method of claim 1, wherein generating the first plurality of re-constructed views comprises:

generating a decoded tiled activation based on the first shape embedding and a third CNN block;

partitioning the decoded tiled activation into a plurality of decoded view activations; and

for each decoded view activation included in the plurality of decoded view activations, generating a different re-constructed view included in the first plurality of re-constructed views based on a fourth CNN block.

6. The method of claim 5, further comprising modifying one or more machine learning parameters associated with a fifth CNN block based on a loss function to generate the third CNN block.

7. The method of claim 1, further comprising, for each virtual camera included in a plurality of virtual cameras, rendering the first 3D geometry based on the virtual camera to generate a different view included in the first plurality of views.

8. The method of claim 1, wherein generating a first view activation included in the view activations occurs at least partially in parallel with generating a second view activation included in the view activations.

9. The method of claim 1, wherein the second shape embedding is associated with a 3D design that is automatically generated via a generative design flow.

10. The method of claim 1, wherein the second shape embedding is associated with a second 3D geometry, and further comprising:

generating a third shape embedding having the first size based on the trained encoder and a third 3D geometry; and

performing one or more comparison operations between the second shape embedding and the third shape embedding when exploring a design space.

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 view included in a first plurality of views associated with a first 3D geometry, generating a view activation based on a first convolutional neural network (CNN) block;

aggregating the view activations to generate a first tiled activation;

generating a first shape embedding having a fixed size based on the first tiled activation and a second CNN block;

generating a first plurality of re-constructed views based on the first shape embedding;

performing one or more training operations on at least one of the first CNN block or the second CNN block based on the first plurality of views and the first plurality of re-constructed views to generate a trained encoder; and

generating a second shape embedding having the fixed size based on the trained encoder.

12. The one or more non-transitory computer readable media of claim 11, wherein aggregating the view activations comprises concatenating the view activations along a single dimension.

13. The one or more non-transitory computer readable media of claim 11, wherein the second CNN block includes one or more 2D CNNs followed by a fully connected layer.

14. The one or more non-transitory computer readable media of claim 11, wherein performing the one or more training operations comprises:

determining that the first CNN block is not trained based on a loss function;

modifying one or more machine learning parameters associated with the first CNN block based on the loss function to generate a third CNN block;

generating a second plurality of re-constructed views based on the third CNN block and the second CNN block; and

determining that the third CNN block and the second CNN block comprise the trained encoder based on the loss function;

the first plurality of views; and

the second plurality of re-constructed views.

15. The one or more non-transitory computer readable media of claim 14, wherein the loss function measures a difference between a plurality of views and a plurality of re-constructed views.

16. The one or more non-transitory computer readable media of claim 11, wherein generating the first plurality of re-constructed views comprises:

performing a first set of convolution operations on the first shape embedding to generate a decoded tiled activation;

partitioning the decoded tiled activation into a plurality of decoded view activations; and

for each decoded view activation included in the plurality of decoded view activations, performing a second set of convolution operations on the decoded view activation to generate a different re-constructed view included in the first plurality of re-constructed views.

17. The one or more non-transitory computer readable media of claim 11, wherein each view included in the first plurality of views comprises a different rendering.

18. The one or more non-transitory computer readable media of claim 11, wherein generating a first view activation included in the view activations occurs at least partially in parallel with generating a second view activation included in the view activations.

19. The one or more non-transitory computer readable media of claim 11, wherein the second shape embedding is associated with a second 3D geometry, and further comprising:

generating a third shape embedding having the first size based on the trained encoder and a third 3D geometry; and

performing one or more comparison operations between the second shape embedding and the third shape embedding when exploring a design space.

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 view included in a first plurality of views associated with a first 3D geometry, generate a view activation based on a first convolutional neural network (CNN) block;

aggregate the view activations to generate a first tiled activation;

generate a first shape embedding having a fixed size based on the first tiled activation and a second CNN block;

generate a first plurality of re-constructed views based on the first shape embedding;

perform one or more training operations on at least one of the first CNN block or the second CNN block based on the first plurality of views and the first plurality of re-constructed views to generate a trained encoder; and

generate a second shape embedding having the fixed size based on the trained encoder.