公开(公告)号：US20250078294A1

公开(公告)日：2025-03-06

申请号：US18458654

申请日：2023-08-30

Applicant: QUALCOMM Incorporated

Inventor： Varun Ravi Kumar ,  Debasmit Das ,  Senthil Kumar Yogamani

IPC: G06T7/50 ,  G06T7/11

Abstract: A method includes receiving one or more images, wherein at least one of the one or more images depicts a water region and analyzing, by one or more processors, the one or more images using a first machine learning model to determine a depth of the water region. The method also includes analyzing, by the one or more processors, the one or more images using a second machine learning model to determine a surface normal of the water region and performing, by the one or more processors, using a third machine learning model, multi-class segmentation of the one or more images. Additionally, the method includes performing one or more fusion operations on outputs of at least two of the first machine learning model, the second machine learning model and the third machine learning model to generate a classification of the water region.

公开(公告)号：US12260571B2

公开(公告)日：2025-03-25

申请号：US17650027

申请日：2022-02-04

Applicant: QUALCOMM Incorporated

Inventor： Hong Cai ,  Shichong Peng ,  Janarbek Matai ,  Jamie Menjay Lin ,  Debasmit Das ,  Fatih Murat Porikli

IPC: G06K9/00 ,  G06N3/045 ,  G06T7/10 ,  G06T7/50

Abstract: 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：US20240395007A1

公开(公告)日：2024-11-28

申请号：US18321520

申请日：2023-05-22

Applicant: QUALCOMM Incorporated

Inventor： Varun Ravi Kumar ,  Debasmit Das ,  Senthil Kumar Yogamani

IPC: G06V10/26 ,  G05D1/02 ,  G06T7/10 ,  G06V10/80

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 a plurality of image frames by a computing device and using machine learning models to identify corrupted or occluded image frames. A first machine learning model may identify corrupted image frames, while a second machine learning model may identify partially occluded image frames. The method may further include generating updated versions of image frames captured by vehicle cameras, such as based on feature vectors from the first and second machine learning models. The feature vectors may be fused and provided to a third machine learning model to generate updated versions of occluded image frames. The method may further include determining vehicle control instructions based on the updated versions. Other aspects and features are also claimed and described.

公开(公告)号：US20250095354A1

公开(公告)日：2025-03-20

申请号：US18467657

申请日：2023-09-14

Applicant: QUALCOMM Incorporated

Inventor： Varun Ravi Kumar ,  Debasmit Das ,  Senthil Kumar Yogamani

IPC: G06V10/86 ,  G06T3/00 ,  G06T5/00 ,  G06T7/194 ,  G06T7/55 ,  G06V10/80 ,  G06V10/82 ,  G06V20/58

Abstract: An apparatus includes a memory and processing circuitry in communication with the memory. The processing circuitry is configured to process a joint graph representation using a graph neural network (GNN) to form an enhanced graph representation. The joint graph representation includes first features from a voxelized point cloud, and second features from a plurality of camera images. The enhanced graph representation includes enhanced first features and enhanced second features. The processing circuitry is further configured to perform a diffusion processes on the enhanced first features and the enhanced second features of the enhanced graph representation to form a denoised graph representation having denoised first features and denoised second features, and fuse the denoised first features and the denoised second features of the denoised graph representation using a graph attention network (GAT) to form a fused point cloud having fused features.

公开(公告)号：US20250086946A1

公开(公告)日：2025-03-13

申请号：US18463756

申请日：2023-09-08

Applicant: QUALCOMM Incorporated

Inventor： Debasmit Das ,  Mohsen Ghafoorian ,  Oleksandr Bailo ,  Yu Fu ,  Hyojin Park ,  Shubhankar Mangesh Borse ,  Fatih Murat Porikli

IPC: G06V10/776 ,  G06T7/11 ,  G06T7/174 ,  G06T7/80 ,  G06V10/74 ,  G06V10/774

Abstract: A system stores first and second images generated by first and second cameras; applies a segmentation model to the first image to generate a first segmentation mask identifying object instances; applies the segmentation model to the second image to generate a second segmentation mask identifying the object instances; projects the first segmentation mask to a viewpoint of the second camera to generate a first projected segmentation mask; converts the first projected segmentation mask and the second segmentation mask to first and second semantic masks, respectively; and computes a first similarity value based on the first and second semantic masks. This may be repeated exchanging the first and second images to compute a second similarity value. The system determines a loss value based on the first similarity value and the second similarity value and trains the segmentation model based on the loss value.

公开(公告)号：US12019726B2

公开(公告)日：2024-06-25

申请号：US17655506

申请日：2022-03-18

Applicant: QUALCOMM Incorporated

Inventor： Debasmit Das ,  Sungrack Yun ,  Fatih Murat Porikli

IPC: G06F15/16 ,  G06F9/54 ,  G06F21/32 ,  G06N5/022 ,  H04L29/06

CPC classification number: G06F21/32 ,  G06N5/022

Abstract: Certain aspects of the present disclosure provide techniques for improved domain adaptation in machine learning. A feature tensor is generated by processing input data using a feature extractor. A first set of logits is generated by processing the feature tensor using a domain-agnostic classifier, and a second set of logits is generated by processing the feature tensor using a domain-specific classifier. A loss is computed based at least in part on the first set of logits and the second set of logits, where the loss includes a divergence loss component. The feature extractor, the domain-agnostic classifier, and the domain-specific classifier are refined using the loss.