Dense 3D Object Reconstruction from a Single Depth View

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2019-12, Vol.41 (12), p.2820-2834
Hauptverfasser:	Yang, Bo, Rosa, Stefano, Markham, Andrew, Trigoni, Niki, Wen, Hongkai
Format:	Artikel
Sprache:	eng
Schlagworte:	3D Reconstruction adversarial learning Coders conditional GAN Datasets Encoders-Decoders Generative adversarial networks Image reconstruction Occupancy Periodic structures Reconstruction shape completion shape inpainting single depth view Solid modeling Task analysis Three-dimensional displays
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