Stereoscopic Dataset from A Video Game: Detecting Converged Axes and Perspective Distortions in S3D Videos

Авторы: Malyshev Kirill, Lavrushkin Sergey, Vatolin Dmitriy
Сборник: 2020 International Conference on 3D Immersion (IC3D)
Год издания: 2020
Место издания: IEEE
DOI: 10.1109/ic3d51119.2020.9376375
Аннотация: This paper presents a method for generating stereoscopic or multi-angle video frames using a computer game (Grand Theft Auto V). We developed a mod that captures synthetic frames allows us to create geometric distortions like those that occur in a real video. These distortions are the main cause of viewer discomfort when watching 3D movies. Datasets generated in this way can aid in solving problems related to machine-learning-based assessment of stereoscopic- or multi-angle-video quality. We trained a convolutional neural network to evaluate perspective distortions and converged camera axes in stereoscopic video, then tested it on real 3D movies. The neural network discovered multiple examples of these distortions.
Добавил в систему: Лаврушкин Сергей Валерьевич

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