Robert Neil Colburn

US Patent:

20210248811, Aug 12, 2021

Filed:

Jan 8, 2021

Appl. No.:

17/145232

Inventors:

- Cupertino CA, US
Joshua SUSSKIND - San Jose CA, US
Aditya SANKAR - Seattle WA, US
Robert Alex COLBURN - Seattle WA, US
Emilien DUPONT - Seattle WA, US
Miguel Angel BAUTISTA MARTIN - San Francisco CA, US

International Classification:

G06T 15/20
G06T 3/60
G06N 3/08

Abstract:

The subject technology provides a framework for learning neural scene representations directly from images, without three-dimensional (3D) supervision, by a machine-learning model. In the disclosed systems and methods, 3D structure can be imposed by ensuring that the learned representation transforms like a real 3D scene. For example, a loss function can be provided which enforces equivariance of the scene representation with respect to 3D rotations. Because naive tensor rotations may not be used to define models that are equivariant with respect to 3D rotations, a new operation called an invertible shear rotation is disclosed, which has the desired equivariance property. In some implementations, the model can be used to generate a 3D representation, such as mesh, of an object from an image of the object.