Invention Grant
- Patent Title: Systems and methods for self-supervised learning of camera intrinsic parameters from a sequence of images
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Application No.: US17692357Application Date: 2022-03-11
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Publication No.: US12175708B2Publication Date: 2024-12-24
- Inventor: Vitor Guizilini , Adrien David Gaidon , Rares A. Ambrus , Igor Vasiljevic , Jiading Fang , Gregory Shakhnarovich , Matthew R. Walter
- Applicant: Toyota Research Institute, Inc.
- Applicant Address: US CA Los Altos
- Assignee: Toyota Research Institute, Inc.
- Current Assignee: Toyota Research Institute, Inc.
- Current Assignee Address: US CA Los Altos
- Agency: Darrow Mustafa PC
- Agent Christopher G. Darrow
- Main IPC: G06T3/18
- IPC: G06T3/18 ; G06T5/80 ; G06T7/50 ; G06T7/80 ; B60W60/00 ; B64C39/02 ; G05D1/00 ; H04N17/00

Abstract:
Systems and methods described herein relate to self-supervised learning of camera intrinsic parameters from a sequence of images. One embodiment produces a depth map from a current image frame captured by a camera; generates a point cloud from the depth map using a differentiable unprojection operation; produces a camera pose estimate from the current image frame and a context image frame; produces a warped point cloud based on the camera pose estimate; generates a warped image frame from the warped point cloud using a differentiable projection operation; compares the warped image frame with the context image frame to produce a self-supervised photometric loss; updates a set of estimated camera intrinsic parameters on a per-image-sequence basis using one or more gradients from the self-supervised photometric loss; and generates, based on a converged set of learned camera intrinsic parameters, a rectified image frame from an image frame captured by the camera.
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