Conference material: "Proceedings of the International Conference on Computer Graphics and Vision “Graphicon” (19-21 September 2023, Moscow)"
Authors:Zhdanov A.D., Zhdanov D.D., Khilik E.D.
Automatic Creation and Annotation of RGB-D Images for Training Machine Vision Systems
Due to the active development of artificial intelligence technologies, machine vision, and deep learning, as well as the emergence of RGB?D cameras that allow you to get a three-dimensional image of the scene, more and more attention is paid to various tasks of processing three-dimensional data. One of these problems is the problem of point cloud segmentation, which is used in various fields, from robotics to architecture, and is solved by machine vision methods. The training of machine vision systems requires the creation and annotation of datasets, which takes up a significant part of the design and development time. In this paper, it is proposed to automate the process of creating a dataset using a scripting interpreter and realistic rendering computer systems, which can significantly reduce the time required to create a dataset. An example of creating a dataset, training a neural network on this dataset, and using a network trained on this dataset to classify objects in a scene image is given.
Machine vision, neural networks, dataset, deep learning, realistic rendering