公开(公告)号：US12198275B2

公开(公告)日：2025-01-14

申请号：US17689851

申请日：2022-03-08

Applicant: Apple Inc.

Inventor： Miguel Angel Bautista Martin ,  Nitish Srivastava ,  Joshua M. Susskind ,  Terrance Devries

IPC: G06T19/00 ,  G06T15/55 ,  G06T19/20

Abstract: Implementations of the subject technology relate to generative scene networks (GSNs) that are able to generate realistic scenes that can be rendered from a free moving camera at any location and orientation. A GSN may be implemented using a global generator and a locally conditioned radiance field. GSNs may employ a spatial latent representation as conditioning for a grid of locally conditioned radiance fields, and may be trained using an adversarial learning framework. Inverting a GSN may allow free navigation of a generated scene conditioned on one or more observations.

公开(公告)号：US11967015B2

公开(公告)日：2024-04-23

申请号：US17145232

申请日：2021-01-08

Applicant: Apple Inc.

Inventor： Qi Shan ,  Joshua Susskind ,  Aditya Sankar ,  Robert Alex Colburn ,  Emilien Dupont ,  Miguel Angel Bautista Martin

IPC: G06T15/20 ,  G06N3/08 ,  G06T3/60

CPC classification number: G06T15/205 ,  G06N3/08 ,  G06T3/60

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.