- Patent Title: Learning-based camera pose estimation from images of an environment
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Application No.: US16872752Application Date: 2020-05-12
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Publication No.: US10964061B2Publication Date: 2021-03-30
- Inventor: Jinwei Gu , Samarth Manoj Brahmbhatt , Kihwan Kim , Jan Kautz
- Applicant: NVIDIA Corporation
- Applicant Address: US CA Santa Clara
- Assignee: NVIDIA Corporation
- Current Assignee: NVIDIA Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Leydig, Voit & Mayer, Ltd.
- Main IPC: G06T7/80
- IPC: G06T7/80 ; G06T7/00 ; G06K9/00 ; G06K9/20 ; G06K9/46 ; G06N3/00 ; G06T7/579 ; G06T7/20

Abstract:
A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.
Public/Granted literature
- US20200273207A1 LEARNING-BASED CAMERA POSE ESTIMATION FROM IMAGES OF AN ENVIRONMENT Public/Granted day:2020-08-27
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