Invention Grant
- Patent Title: Systems and methods for jointly training a machine-learning-based monocular optical flow, depth, and scene flow estimator
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Application No.: US17489237Application Date: 2021-09-29
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Publication No.: US11948310B2Publication Date: 2024-04-02
- Inventor: Vitor Guizilini , Rares A. Ambrus , Kuan-Hui Lee , Adrien David Gaidon
- 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: G06K9/00
- IPC: G06K9/00 ; G05D1/00 ; G06N3/045 ; G06N3/08 ; G06T7/246 ; G06T7/50 ; G06T7/55 ; G06T7/73

Abstract:
Systems and methods described herein relate to jointly training a machine-learning-based monocular optical flow, depth, and scene flow estimator. One embodiment processes a pair of temporally adjacent monocular image frames using a first neural network structure to produce a first optical flow estimate; processes the pair of temporally adjacent monocular image frames using a second neural network structure to produce an estimated depth map and an estimated scene flow; processes the estimated depth map and the estimated scene flow using the second neural network structure to produce a second optical flow estimate; and imposes a consistency loss between the first optical flow estimate and the second optical flow estimate that minimizes a difference between the first optical flow estimate and the second optical flow estimate to improve performance of the first neural network structure in estimating optical flow and the second neural network structure in estimating depth and scene flow.
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