公开(公告)号：US20250061153A1

公开(公告)日：2025-02-20

申请号：US18935222

申请日：2024-11-01

Applicant: Nvidia Corporation

Inventor： Hang Chu ,  Daiqing Li ,  David Jesus Acuna Marrero ,  Amlan Kar ,  Maria Shugrina ,  Ming-Yu Liu ,  Antonio Torralba Barriuso ,  Sanja Fidler

IPC: G06F16/901 ,  G06F30/13 ,  G06F30/27 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N3/084 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20 ,  G06V10/764 ,  G06V10/82 ,  G06V10/84 ,  G06V20/10

Abstract: A generative model can be used for generation of spatial layouts and graphs. Such a model can progressively grow these layouts and graphs based on local statistics, where nodes can represent spatial control points of the layout, and edges can represent segments or paths between nodes, such as may correspond to road segments. A generative model can utilize an encoder-decoder architecture where the encoder is a recurrent neural network (RNN) that encodes local incoming paths into a node and the decoder is another RNN that generates outgoing nodes and edges connecting an existing node to the newly generated nodes. Generation is done iteratively, and can finish once all nodes are visited or another end condition is satisfied. Such a model can generate layouts by additionally conditioning on a set of attributes, giving control to a user in generating the layout.

公开(公告)号：US20200302250A1

公开(公告)日：2020-09-24

申请号：US16825199

申请日：2020-03-20

Applicant: Nvidia Corporation

Inventor： Hang Chu ,  Daiqing Li ,  David Jesus Acuna Marrero ,  Amlan Kar ,  Maria Shugrina ,  Ming-Yu Liu ,  Antonio Torralba Barriuso ,  Sanja Fidler

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20

Abstract: A generative model can be used for generation of spatial layouts and graphs. Such a model can progressively grow these layouts and graphs based on local statistics, where nodes can represent spatial control points of the layout, and edges can represent segments or paths between nodes, such as may correspond to road segments. A generative model can utilize an encoder-decoder architecture where the encoder is a recurrent neural network (RNN) that encodes local incoming paths into a node and the decoder is another RNN that generates outgoing nodes and edges connecting an existing node to the newly generated nodes. Generation is done iteratively, and can finish once all nodes are visited or another end condition is satisfied. Such a model can generate layouts by additionally conditioning on a set of attributes, giving control to a user in generating the layout.