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.: US17489231Application Date: 2021-09-29
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Publication No.: US11948309B2Publication 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: G06T7/50
- IPC: G06T7/50 ; G05D1/00 ; G06N3/045 ; G06N3/08 ; G06T7/246 ; 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 an optical flow estimate and to extract, from at least one image frame in the pair of temporally adjacent monocular image frames, a set of encoded image context features; triangulates the optical flow estimate to generate a depth map; extracts a set of encoded depth context features from the depth map using a depth context encoder; and combines the set of encoded image context features and the set of encoded depth context features to improve performance of a second neural network structure in estimating depth and scene flow.
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