Invention Grant
- Patent Title: Computer-implemented method to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks
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Application No.: US17402349Application Date: 2021-08-13
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Publication No.: US11948272B2Publication Date: 2024-04-02
- Inventor: Hemang Chawla , Arnav Varma , Elahe Arani , Bahram Zonooz
- Applicant: NavInfo Europe B.V.
- Applicant Address: NL Eindhoven
- Assignee: NAVINFO EUROPE B.V.
- Current Assignee: NAVINFO EUROPE B.V.
- Current Assignee Address: NL Eindhoven
- Agency: Peacock Law P.C.
- Agent Justin R. Muehlmeyer
- Priority: EP 207576 2020.11.13
- Main IPC: G06T3/40
- IPC: G06T3/40 ; G06F18/214 ; G06N3/045 ; G06N3/08 ; G06T3/4038 ; G06T3/4046 ; G06T7/50 ; G06V20/40

Abstract:
A computer-implemented method to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein complementary GPS coordinates synchronized with the images are used to calculate a GPS to scale loss to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. A relative weight assigned to the GPS to scale loss exponentially increases as training progresses. The depth and ego-motion prediction neural networks are trained using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions.
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