Preprint Article Version 1 This version is not peer-reviewed

BAGAN: Effective Data Generation Based on GAN Augmented 3D Synthesizing

Version 1 : Received: 8 November 2018 / Approved: 9 November 2018 / Online: 9 November 2018 (16:00:39 CET)

A peer-reviewed article of this Preprint also exists.

Ma, Y.; Liu, K.; Guan, Z.; Xu, X.; Qian, X.; Bao, H. Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing. Symmetry 2018, 10, 734. Ma, Y.; Liu, K.; Guan, Z.; Xu, X.; Qian, X.; Bao, H. Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing. Symmetry 2018, 10, 734.

Journal reference: Symmetry 2018, 10, 734
DOI: 10.3390/sym10120734

Abstract

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

Subject Areas

object recognition; image data synthesizing; Human-computer interaction; data synthesizing for immersive HCI; generative adversarial nets; BAGAN

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