公开(公告)号：US20240161368A1

公开(公告)日：2024-05-16

申请号：US18460903

申请日：2023-09-05

申请人： QUALCOMM Incorporated

发明人： Shubhankar Mangesh BORSE ,  Debasmit DAS ,  Hyojin PARK ,  Hong CAI ,  Risheek GARREPALLI ,  Fatih Murat PORIKLI

IPC分类号： G06T11/60 ,  G06T3/40 ,  G06T7/12 ,  G06T7/50 ,  G06V10/44

CPC分类号： G06T11/60 ,  G06T3/40 ,  G06T7/12 ,  G06T7/50 ,  G06V10/44

摘要： Certain aspects of the present disclosure provide techniques and apparatus for regenerative learning to enhance dense predictions. In one example method, an input image is accessed. A dense prediction output is generated based on the input image using a dense prediction machine learning (ML) model, and a regenerated version of the input image is generated. A first loss is generated based on the input image and a corresponding ground truth dense prediction, and a second loss is generated based on the regenerated version of the input image. One or more parameters of the dense prediction ML model are updated based on the first and second losses.

公开(公告)号：US20240177329A1

公开(公告)日：2024-05-30

申请号：US18481050

申请日：2023-10-04

申请人： QUALCOMM Incorporated

发明人： Hong CAI ,  Yinhao ZHU ,  Jisoo JEONG ,  Yunxiao SHI ,  Fatih Murat PORIKLI

IPC分类号： G06T7/593 ,  G06T3/40 ,  G06T7/246 ,  G06T7/579

CPC分类号： G06T7/593 ,  G06T3/40 ,  G06T7/248 ,  G06T7/579 ,  G06T2207/10012 ,  G06T2207/20081 ,  G06T2207/20084

摘要： Systems and techniques are provided for processing sensor data. For example, a process can include determining, using a trained machine learning system, a predicted depth map for an image, the predicted depth map including a respective predicted depth value for each pixel of the image. The process can further include obtaining depth values for the image, the depth values including depth values for less than all pixels of the image from a tracker configured to determine the depth values based on one or more feature points between frames. The process can further include scaling the predicted depth map for the image using and the depth values. The output of the process can be scale-correct depth prediction values.

公开(公告)号：US20240020848A1

公开(公告)日：2024-01-18

申请号：US18349771

申请日：2023-07-10

申请人： QUALCOMM Incorporated

发明人： Debasmit DAS ,  Shubhankar Mangesh BORSE ,  Hyojin PARK ,  Kambiz AZARIAN YAZDI ,  Hong CAI ,  Risheek GARREPALLI ,  Fatih Murat PORIKLI

IPC分类号： G06T7/168

CPC分类号： G06T7/168 ,  G06T2207/20132

摘要： Systems and techniques are provided for processing one or more images. For instance, according to some aspects of the disclosure, a method may include obtaining an unlabeled image and generating at least one transformed image based on the unlabeled image. The method may include processing the unlabeled image using a pre-trained semantic segmentation model to generate a first segmentation output. The method may further include processing the at least one transformed image using the pre-trained semantic segmentation model to generate at least a second segmentation output. The method may include fine-tuning, based on the first segmentation output and at least the second segmentation output, one or more parameters of the pre-trained semantic segmentation model.

公开(公告)号：US20230004812A1

公开(公告)日：2023-01-05

申请号：US17808949

申请日：2022-06-24

申请人： QUALCOMM Incorporated

发明人： Shubhankar Mangesh BORSE ,  Hong CAI ,  Yizhe ZHANG ,  Fatih Murat PORIKLI

IPC分类号： G06N3/08 ,  G06K9/62

摘要： Certain aspects of the present disclosure provide techniques for training neural networks using hierarchical supervision. An example method generally includes training a neural network with a plurality of stages using a training data set and an initial number of classification clusters into which data in the training data set can be classified. A cluster-validation set performance metric is generated for each stage based on a reduced number of classification clusters relative to the initial number of classification clusters and a validation data set. A number of classification clusters to implement at each stage is selected based on the cluster-validation set performance metric and an angle selected relative to the cluster-validation set performance metric for a last stage of the neural network. The neural network is retrained based on the training data set and the selected number of classification clusters for each stage, and the trained neural network is deployed.

公开(公告)号：US20240303913A1

公开(公告)日：2024-09-12

申请号：US18180797

申请日：2023-03-08

申请人： QUALCOMM Incorporated

发明人： Yinhao ZHU ,  Rui ZHU ,  Hong CAI ,  Fatih Murat PORIKLI

IPC分类号： G06T15/50 ,  G06T7/593

CPC分类号： G06T15/506 ,  G06T7/593

摘要： Systems and techniques are provided for physical-based light estimation for inverse rendering of indoor scenes. For example, a computing device can obtain an estimated scene geometry based on a multi-view observation of a scene. The computing device can further obtain a light emission mask based on the multi-view observation of the scene. The computing device can also obtain an emitted radiance field based on the multi-view observation of the scene. The computing device can then determine, based on the light emission mask and the emitted radiance field, a geometry of at least one light source of the estimated scene geometry.

公开(公告)号：US20240161312A1

公开(公告)日：2024-05-16

申请号：US18477493

申请日：2023-09-28

申请人： QUALCOMM Incorporated

发明人： Jisoo JEONG ,  Risheek GARREPALLI ,  Hong CAI ,  Fatih Murat PORIKLI

IPC分类号： G06T7/246

CPC分类号： G06T7/248 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084

摘要： A computer-implemented method includes generating a first augmented frame by combining a first image and a first frame of a first frame pair. The computer-implemented method also includes generating, via an optical flow estimation model, a first flow estimation based on a second frame of the first frame pair and the first augmented frame. The computer-implemented method further includes updating one or both of parameters or weights of the optical flow estimation model based on a first loss between the first flow estimation and a training target.

公开(公告)号：US20240020844A1

公开(公告)日：2024-01-18

申请号：US18349726

申请日：2023-07-10

申请人： QUALCOMM Incorporated

发明人： Debasmit DAS ,  Shubhankar Mangesh BORSE ,  Hyojin PARK ,  Kambiz AZARIAN YAZDI ,  Hong CAI ,  Risheek GARREPALLI ,  Fatih Murat PORIKLI

IPC分类号： G06T7/11

CPC分类号： G06T7/11 ,  G06T2207/20081 ,  G06T2207/20004

摘要： Systems and techniques are provided for processing data (e.g., image data). For instance, according to some aspects of the disclosure, a method may include receiving, at a transformer of a machine learning system, learnable queries, keys, and values obtained from a feature map of a segmentation model of the machine learning system. The method may further include learning, via the transformer, a mapping between an unsupervised output and a supervised output of the segmentation model based on the feature map.

公开(公告)号：US20240171727A1

公开(公告)日：2024-05-23

申请号：US18470326

申请日：2023-09-19

申请人： QUALCOMM Incorporated

发明人： Yunxiao SHI ,  Hong CAI ,  Fatih Murat PORIKLI ,  Amin ANSARI ,  Sai Madhuraj JADHAV

IPC分类号： H04N13/363 ,  G06T7/50 ,  G06V10/44 ,  G06V10/771 ,  H04N13/351

CPC分类号： H04N13/363 ,  G06T7/50 ,  G06V10/44 ,  G06V10/771 ,  H04N13/351 ,  G06V2201/07

摘要： Systems and techniques are provided for processing image data. For example, a process can include obtaining a plurality of input images associated with a plurality of different spatial views. The process can include generating a set of features based on the plurality of input images. The process can include generating a set of projected features based on the set of features, wherein an embedding size associated with the set of projected features is smaller than an embedding size associated with the set of features. The process can include determining a cross-view attention associated with the plurality of different spatial views, the cross-view attention determined using the set of projected features.

公开(公告)号：US20230252658A1

公开(公告)日：2023-08-10

申请号：US17650027

申请日：2022-02-04

申请人： QUALCOMM Incorporated

发明人： Hong CAI ,  Shichong PENG ,  Janarbek MATAI ,  Jamie Menjay LIN ,  Debasmit DAS ,  Fatih Murat PORIKLI

IPC分类号： G06T7/50 ,  G06T7/10 ,  G06N3/04

CPC分类号： G06T7/50 ,  G06T7/10 ,  G06N3/0454 ,  G06T2207/20084 ,  G06T2207/20212

摘要： Certain aspects of the present disclosure provide techniques for generating fine depth maps for images of a scene based on semantic segmentation and segment-based refinement neural networks. An example method generally includes generating, through a segmentation neural network, a segmentation map based on an image of a scene. The segmentation map generally comprises a map segmenting the scene into a plurality of regions, and each region of the plurality of regions is generally associated with one of a plurality of categories. A first depth map of the scene is generated through a first depth neural network based on a depth measurement of the scene. A second depth map of the scene is generated through a depth refinement neural network based on the segmentation map and the first depth map. One or more actions are taken based on the second depth map of the scene.

公开(公告)号：US20230154005A1

公开(公告)日：2023-05-18

申请号：US17807614

申请日：2022-06-17

申请人： QUALCOMM Incorporated

发明人： Shubhankar Mangesh BORSE ,  Hyojin PARK ,  Hong CAI ,  Debasmit DAS ,  Risheek GARREPALLI ,  Fatih Murat PORIKLI

IPC分类号： G06T7/10 ,  G06N3/08

CPC分类号： G06T7/10 ,  G06N3/08 ,  G06T2207/20084 ,  G06T2207/20081

摘要： Aspects of the present disclosure relate to a novel framework for integrating both semantic and instance contexts for panoptic segmentation. In one example aspect, a method for processing image data includes: processing semantic feature data and instance feature data with a panoptic encoding generator to generate a panoptic encoding; processing the panoptic encoding to generate a panoptic segmentation features; and generating the panoptic segmentation mask based on the panoptic segmentation features.