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

A device configured to provide 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, wherein the device is configured to:

• provide multiple synthetic dense depth map data items from CAD data of various synthetic scenes;
• provide multiple real raw depth map data items obtained from real-world depth sensor measurements of real-world scenes;
• train a generative model to obtaining a trained generator model for generating generated raw depth map data items from the synthetic dense depth map data items;
• and
• apply the trained generator model to generate training data from provided synthetic dense depth map data.

A non-transitory machine readable medium on which is stored a computer program for providing training data for training of a data-driven model as a machine-learning model, wherein the data-driven model is to be trained to generate dense depth maps from sensor acquired raw depth maps, the computer program, when executed by a computer, causing the computer to perform the following steps:

• 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 to obtain a trained generator model for generating generated raw depth map data items from the synthetic dense depth map data items;
• and
• applying the trained generator model to generate training data from provided synthetic dense depth map data.