公开(公告)号：US20250037353A1

公开(公告)日：2025-01-30

申请号：US18714862

申请日：2022-01-13

Applicant: Google LLC

Inventor： Wen-Sheng Chu ,  Dmitry Lagun ,  Ioannis Daras ,  Abhishek Kumar

IPC: G06T15/20 ,  G06N3/08

Abstract: Systems and methods for training a generative neural radiance field model can include geometric regularization. Geometric regularization can involve the utilization of reference geometry data and/or an output of a surface prediction model. The geometry regularization can train the generative neural radiance field model to mitigate artifact generation by limiting a distribution considered for color value prediction and density value prediction to a range associated with a realistic geometry range.

公开(公告)号：US20240193903A1

公开(公告)日：2024-06-13

申请号：US18078634

申请日：2022-12-09

Applicant: Google LLC

Inventor： Skirmantas Kligys ,  Wen-Sheng Chu ,  Xiaoming Liu

IPC: G06V10/25 ,  G06T7/13 ,  G06V20/70

CPC classification number: G06V10/25 ,  G06T7/13 ,  G06V20/70 ,  G06T2207/20016 ,  G06T2207/20084 ,  G06V2201/07

Abstract: Provided are systems and methods for detecting an object in an image. The method can include receiving an input image and analyzing the input image using an image segmentation model to identify one or more indicative areas within the input image, the one or more indicative areas being indicative of one or more objects within the input image. The method can also include analyzing the one or more indicative areas of the input image using a convolutional model to generate at least one label for at least one portion of the one or more indicative areas of the input image, the label indicating whether a specific object is identified within the input image, and performing at least one action based on the at least one label for the at least one portion.

公开(公告)号：US12008821B2

公开(公告)日：2024-06-11

申请号：US17314738

申请日：2021-05-07

Applicant: Google LLC

Inventor： Wen-Sheng Chu ,  Abhishek Kumar ,  Min Jin Chong

IPC: G06K9/00 ,  G06F18/214 ,  G06F18/23 ,  G06N3/08 ,  G06N20/00 ,  G06T7/73 ,  G06V20/64 ,  G06V40/10 ,  G06V40/16

CPC classification number: G06V20/64 ,  G06F18/214 ,  G06F18/23 ,  G06N3/08 ,  G06N20/00 ,  G06T7/73 ,  G06V40/103 ,  G06V40/171

Abstract: Systems and methods of the present disclosure are directed to a computer-implemented method. The method can include obtaining a first image depicting a first object and a second image depicting a second object, wherein the first object comprises a first feature set and the second object comprises a second feature set. The method can include processing the first image with a machine-learned image transformation model comprising a plurality of model channels to obtain a first channel mapping indicative of a mapping between the plurality of model channels and the first feature set. The method can include processing the second image with the model to obtain a second channel mapping indicative of a mapping between the plurality of model channels and the second feature set. The method can include generating an interpolation vector for a selected feature.

公开(公告)号：US20220374625A1

公开(公告)日：2022-11-24

申请号：US17314738

申请日：2021-05-07

Applicant: Google LLC

Inventor： Wen-Sheng Chu ,  Abhishek Kumar ,  Min Jin Chong

IPC: G06K9/00 ,  G06N20/00 ,  G06K9/62 ,  G06T7/73 ,  G06N3/08

Abstract: Systems and methods of the present disclosure are directed to a computer-implemented method. The method can include obtaining a first image depicting a first object and a second image depicting a second object, wherein the first object comprises a first feature set and the second object comprises a second feature set. The method can include processing the first image with a machine-learned image transformation model comprising a plurality of model channels to obtain a first channel mapping indicative of a mapping between the plurality of model channels and the first feature set. The method can include processing the second image with the model to obtain a second channel mapping indicative of a mapping between the plurality of model channels and the second feature set. The method can include generating an interpolation vector for a selected feature.

公开(公告)号：US11205064B1

公开(公告)日：2021-12-21

申请号：US16901564

申请日：2020-06-15

Applicant: Google LLC

Inventor： Wen-Sheng Chu ,  Sam Ekong ,  Kuntal Sengupta

IPC: G06K9/00 ,  G01B11/22 ,  G06K9/46

Abstract: Methods are provided to determine a quality score for depth map. The quality score is calculated from metrics that detect artifacts or other inaccuracies in the depth map such as flat patches, artifactual edges, and patchy regions. A flatness metric detects regions of neighboring pixels that have substantially the same depth value. A jaggedness metric detects hard edges or other discontinuities. A patchiness metric detects regions that are wholly enclosed by an edge and that have sub-threshold areas. The individual metrics are normalized and combined to determine an overall quality score for the depth map. The quality score can then be compared to one or more thresholds to determine a quality label for the depth map. Such a quality label can then be used to unlock a device, to invalidate an unlock attempt, to recalibrate a depth sensor, or to perform some other operations.