- Patent Title: Method and device for generating training data to generate synthetic real-world-like raw depth maps for the training of domain-specific models for logistics and manufacturing tasks
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Application No.: US17883935Application Date: 2022-08-09
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Publication No.: US12293572B2Publication Date: 2025-05-06
- Inventor: Yakov Miron , Yoel Shapiro
- Applicant: Robert Bosch GmbH
- Applicant Address: DE Stuttgart
- Assignee: Robert Bosch GmbH
- Current Assignee: Robert Bosch GmbH
- Current Assignee Address: DE Stuttgart
- Agency: NORTON ROSE FULBRIGHT US LLP
- Agent Gerard A. Messina
- Main IPC: G06V10/774
- IPC: G06V10/774 ; G06V10/82

Abstract:
A computer-implemented method for providing training data for training of a data-driven depth completion model as a machine-learning model, wherein the depth completion model is to be trained to generate dense depth maps from sensor acquired raw depth maps. The method includes: providing multiple synthetic dense depth map data items from CAD data of various synthetic scenes; providing multiple real raw depth map data items obtained from real-world depth sensor measurements of real-world scenes; training a generative model for obtaining a trained generator model for generating generated raw depth map data items from the synthetic dense depth map data items; applying the trained generator model to generate training data from provided synthetic dense depth map data.
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