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

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.