-
公开(公告)号：US20240119697A1
公开(公告)日：2024-04-11
申请号：US18012264
申请日：2022-10-10
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Suhani Deepak-Ranu Vora , Noha Radwan , Klaus Greff , Henning Meyer , Kyle Adam Genova , Seyed Mohammad Mehdi Sajjadi , Etienne François Régis Pot , Andrea Tagliasacchi
CPC classification number: G06V10/26 , G06T7/143 , G06T15/08 , G06T2207/20076 , G06T2207/20081
Abstract: Example embodiments of the present disclosure provide an example computer-implemented method for constructing a three-dimensional semantic segmentation of a scene from two-dimensional inputs. The example method includes obtaining, by a computing system comprising one or more processors, an image set comprising one or more views of a subject scene. The example method includes generating, by the computing system and based at least in part on the image set, a scene representation describing the subject scene in three dimensions. The example method includes generating, by the computing system and using a machine-learned semantic segmentation model framework, a multidimensional field of probability distributions over semantic categories, the multidimensional field defined over the three dimensions of the subject scene. The example method includes outputting, by the computing system, classification data for at least one location in the subject scene.
-
公开(公告)号：US20240273811A1
公开(公告)日：2024-08-15
申请号：US18012270
申请日：2022-10-24
Applicant: Google LLC
Inventor： Noha Radwan , Jonathan Tilton Barron , Benjamin Joseph Mildenhall , Seyed Mohammad Mehdi Sajjadi , Michael Niemeyer
CPC classification number: G06T15/205 , G06V10/82
Abstract: Systems and methods for training a neural radiance field model can include the use of image patches for ground truth training. For example, the systems and methods can include generating patch renderings with a neural radiance field model, comparing the patch renderings to ground truth patches from ground truth images, and adjusting one or more parameters based on the comparison. Additionally and/or alternatively, the systems and methods can include the utilization of a flow model for mitigating and/or minimizing artifact generation.
-
Patent Agency Ranking
公开(公告)号：US20240096001A1
公开(公告)日：2024-03-21
申请号：US18013983
申请日：2022-11-15
Applicant: Google LLC
Inventor： Seyed Mohammad Mehdi Sajjadi , Henning Meyer , Etienne François Régis Pot , Urs Michael Bergmann , Klaus Greff , Noha Radwan , Suhani Deepak-Ranu Vora , Mario Lu¢i¢ , Daniel Christopher Duckworth , Thomas Allen Funkhouser , Andrea Tagliasacchi
Abstract: Provided are machine learning models that generate geometry-free neural scene representations through efficient object-centric novel-view synthesis. In particular, one example aspect of the present disclosure provides a novel framework in which an encoder model (e.g., an encoder transformer network) processes one or more RGB images (with or without pose) to produce a fully latent scene representation that can be passed to a decoder model (e.g., a decoder transformer network). Given one or more target poses, the decoder model can synthesize images in a single forward pass. In some example implementations, because transformers are used rather than convolutional or MLP networks, the encoder can learn an attention model that extracts enough 3D information about a scene from a small set of images to render novel views with correct projections, parallax, occlusions, and even semantics, without explicit geometry.
-
公开(公告)号：US11308659B2
公开(公告)日：2022-04-19
申请号：US17390263
申请日：2021-07-30
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Seyed Mohammad Mehdi Sajjadi , Jonathan Tilton Barron , Noha Radwan , Alexey Dosovitskiy , Ricardo Martin-Brualla
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
-
公开(公告)号：US20250014236A1
公开(公告)日：2025-01-09
申请号：US18891789
申请日：2024-09-20
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin-Brualla , Jonathan Tilton Barron , Noha Radwan , Seyed Mohammad Mehdi Sajjadi
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
-
公开(公告)号：US12100074B2
公开(公告)日：2024-09-24
申请号：US18327609
申请日：2023-06-01
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin-Brualla , Jonathan Tilton Barron , Noha Radwan , Seyed Mohammad Mehdi Sajjadi
CPC classification number: G06T11/001 , G06T7/90 , G06T2207/20081
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
-
公开(公告)号：US20230306655A1
公开(公告)日：2023-09-28
申请号：US18327609
申请日：2023-06-01
Applicant: Google LLC
Inventor： Daniel Christopher Duckworth , Alexey Dosovitskiy , Ricardo Martin-Brualla , Jonathan Tilton Barron , Noha Radwan , Seyed Mohammad Mehdi Sajjadi
CPC classification number: G06T11/001 , G06T7/90 , G06T2207/20081
Abstract: Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, “in the wild” photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).
-
-
-
-
-
-
Search Results
7
records
(took 0.022 seconds)
