公开(公告)号：US20240303841A1

公开(公告)日：2024-09-12

申请号：US18538869

申请日：2023-12-13

Applicant: QUALCOMM Incorporated

Inventor： Rajeev YASARLA ,  Hong CAI ,  Jisoo JEONG ,  Risheek GARREPALLI ,  Yunxiao SHI ,  Fatih Murat PORIKLI

IPC: G06T7/50 ,  G06T7/246 ,  G06T11/60 ,  G06V10/44 ,  G06V10/62

CPC classification number: G06T7/50 ,  G06T7/246 ,  G06T11/60 ,  G06V10/44 ,  G06V10/62

Abstract: Disclosed are systems and techniques for capturing images (e.g., using a monocular image sensor) and detecting depth information. According to some aspects, a computing system or device can generate a feature representation of a current image and update accumulated feature information for storage in a memory based on a feature representation of a previous image and optical flow information of the previous image. The accumulated feature information can include accumulated image feature information associated with a plurality of previous images and accumulated optical flow information associated of the plurality of previous images. The computing system or device can obtain information associated with relative motion of the current image based on the accumulated feature information and the feature representation of the current image. The computing system or device can estimate depth information for the current image based on the information associated with the relative motion and the accumulated feature information.

公开(公告)号：US20240169542A1

公开(公告)日：2024-05-23

申请号：US18346470

申请日：2023-07-03

Applicant: QUALCOMM Incorporated

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

IPC: G06T7/10 ,  G06T5/20 ,  G06T5/50 ,  G06V10/44 ,  G06V10/80

CPC classification number: G06T7/10 ,  G06T5/20 ,  G06T5/50 ,  G06V10/44 ,  G06V10/806 ,  G06T2207/20221

Abstract: Techniques and systems are provided for generating one or more segmentations masks. For instance, a process may include generating a delta image based on a difference between a current image and a prior image. The process may further include processing, using a transform operation, the delta image and features representing the prior image to generate a transformed feature representation of the prior image. The process may include combining the transformed feature representation of the prior image with features representing the current image to generate a combined feature representation of the current image. The process may further include generating, based on the combined feature representation of the current image, a segmentation mask for the current image.

公开(公告)号：US20240070812A1

公开(公告)日：2024-02-29

申请号：US18358857

申请日：2023-07-25

Applicant: QUALCOMM Incorporated

Inventor： Risheek GARREPALLI ,  Rajeswaran CHOCKALINGAPURAMRAVINDRAN ,  Jisoo JEONG ,  Fatih Murat PORIKLI

IPC: G06T3/40 ,  G06T7/579

CPC classification number: G06T3/4053 ,  G06T7/579

Abstract: A processor-implemented method comprises processing a single level cost volume across multiple processing stages by varying a receptive field across each of the processing stages. The method also includes performing a learning-based correspondence estimation task based on the processing. The varying may include processing a different resolution of the cost volume at each processing stage while maintaining a same neighborhood sampling radius. The resolution may increase from a first processing stage to a later processing stage. The varying may also include varying a neighborhood sampling radius at each of the processing stages while maintaining a same resolution. The task may be optical flow estimation or stereo estimation.

公开(公告)号：US20230154005A1

公开(公告)日：2023-05-18

申请号：US17807614

申请日：2022-06-17

Applicant: QUALCOMM Incorporated

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

IPC: G06T7/10 ,  G06N3/08

CPC classification number: G06T7/10 ,  G06N3/08 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: 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.

公开(公告)号：US20250124301A1

公开(公告)日：2025-04-17

申请号：US18488779

申请日：2023-10-17

Applicant: QUALCOMM Incorporated

Inventor： Amirhossein HABIBIAN ,  Risheek GARREPALLI ,  Fatih Murat PORIKLI

IPC: G06N3/096 ,  G06N3/0455 ,  G06N3/0464 ,  G06N3/0475 ,  G06T11/00

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. During a first iteration of processing data using a denoising backbone of a diffusion machine learning model, a first latent tensor is generated using a lower resolution block of the denoising backbone, and a first feature tensor is generated based on processing the first latent tensor using a higher resolution block of the denoising backbone, the higher resolution block using a higher resolution than the lower resolution block. A second latent tensor is generated based on processing the first latent tensor using an adapter block of the denoising backbone. During a second iteration of processing the data using the denoising backbone, a second feature tensor is generated based on processing the second latent tensor using the higher resolution block.

公开(公告)号：US20240020848A1

公开(公告)日：2024-01-18

申请号：US18349771

申请日：2023-07-10

Applicant: QUALCOMM Incorporated

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

IPC: G06T7/168

CPC classification number: G06T7/168 ,  G06T2207/20132

Abstract: 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：US20250095182A1

公开(公告)日：2025-03-20

申请号：US18468656

申请日：2023-09-15

Applicant: QUALCOMM Incorporated

Inventor： Jisoo JEONG ,  Hong CAI ,  Babak EHTESHAMI BEJNORDI ,  Risheek GARREPALLI ,  Rajeev YASARLA ,  Fatih Murat PORIKLI

IPC: G06T7/593 ,  G06T7/246 ,  G06T7/285

Abstract: Techniques and systems are provided for image processing. For instance, a process can include correlating a first set of features from a first viewpoint with a second set of features from a second viewpoint at a first time period to generate a first disparity cost volume; correlating a third set of features from the first viewpoint at a second time period with the first set of features to generate a first optical flow cost volume; gating the first disparity cost volume to generate first intermediate disparity information; gating the first optical flow cost volume to generate first intermediate optical flow information; correlating the first set of features, the second set of features, and the first intermediate optical flow information to generate disparity information for output; and correlating the third set of features, the first set of features, and the first intermediate disparity information to generate optical flow information for output.

公开(公告)号：US20240161368A1

公开(公告)日：2024-05-16

申请号：US18460903

申请日：2023-09-05

Applicant: QUALCOMM Incorporated

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

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

CPC classification number: G06T11/60 ,  G06T3/40 ,  G06T7/12 ,  G06T7/50 ,  G06V10/44

Abstract: 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：US20230186487A1

公开(公告)日：2023-06-15

申请号：US17549768

申请日：2021-12-13

Applicant: QUALCOMM Incorporated

Inventor： Rajeswaran CHOCKALINGAPURAMRAVINDRAN ,  Kristopher URQUHART ,  Jamie Menjay LIN ,  Risheek GARREPALLI

IPC: G06T7/215

CPC classification number: G06T7/215 ,  G06T2207/10016 ,  G06T2207/20084

Abstract: A computer-implemented method includes receiving a first input. The first input is interpolated based on a first shift along a first dimension and a second shift along a second dimension. A first output is generated based on the interpolated first input. The first output corresponds to a vectorized bilinear shift of the first input for use in place of grid sampling algorithms.

公开(公告)号：US20250131606A1

公开(公告)日：2025-04-24

申请号：US18492572

申请日：2023-10-23

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Risheek GARREPALLI ,  Qiqi HOU ,  Jisoo JEONG ,  Shreya KADAMBI ,  Munawar HAYAT ,  Fatih Murat PORIKLI

IPC: G06T11/00 ,  G06T3/40

Abstract: A processor-implemented method includes receiving a text-semantic input at a first stage of a neural network, including a first convolutional block and no attention layers. The method receives, at a second stage, a first output from the first stage. The second stage comprises a first down sampling block including a first attention layer and a second convolutional block. The method receives, at a third stage, a second output from the second stage. The third stage comprises a first up sampling block including a second attention layer and a first set of convolutional blocks. The method receives, at a fourth stage, the first output from the first stage and a third output from the third stage. The fourth stage comprises a second up sampling block including no attention layers and a second set of convolutional blocks. The method generates an image at the fourth stage, based on the text-semantic input.