公开(公告)号：US20250085407A1

公开(公告)日：2025-03-13

申请号：US18464769

申请日：2023-09-11

Applicant: QUALCOMM Incorporated

Inventor： Varun Ravi Kumar ,  Senthil Kumar Yogamani ,  Shivansh Rao

IPC: G01S7/497 ,  G01S17/86 ,  G01S17/931

Abstract: A method includes receiving a plurality of images, wherein a first image of the one or more images comprises a range image and a second image comprises a camera image and filtering the first image to generate a filtered first image. The method also includes generating a plurality of depth estimates based on the second image and generating an attention map by combining the filtered first image and the plurality of depth estimates. Additionally, the method includes generating a consistency score indicative of a consistency of depth estimates between the first image and the second image based on the attention map, modulating one or more features extracted from the second image based on the consistency score using a gating mechanism to generate modulated one or more features, and generating a classification of one or more soiled regions in the second image based on the modulated one or more features.

公开(公告)号：US20240400079A1

公开(公告)日：2024-12-05

申请号：US18329416

申请日：2023-06-05

Applicant: QUALCOMM Incorporated

Inventor： Sweta Priyadarshi ,  Shivansh Rao ,  Varun Ravi Kumar ,  Senthil Kumar Yogamani

IPC: B60W50/14 ,  G06T7/00

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving, by a processor, image data from a camera image sensor; receiving, by the processor, point cloud data from a light detection and ranging (LiDAR) sensor; generating, by the processor and using a first machine learning model, fused image data that combines the image data and the point cloud data; and determining, by the processor and using a second machine learning model, whether the fused image data satisfies a criteria based on whether a population risk function of the first machine learning model exceeds a threshold. Other aspects and features are also claimed and described.