Multi-view 3D Models Completion with Generative Adversarial Networks

Multi-view 3D Models Completion with Generative Adversarial Networks (MSc)

[1] proposes a method to infer 3D representation of object from a single image. it fushes into a full 3D point cloud based on the single image which is generated the unseen views and their depth maps, then furhter optimizes the 3D point cloud to obtain mesh.

[3] recently has incredible performance on semi-supervised and unsupervised image generation. Given images and noise, it can generate a seemingly reasonable image, such as face generation, scene generation, digital generation and so on. We hope that using GANs to improve the previously unseen view prediction.

In this thesis, you will imporve [1] method which focus on the more applied dataset and unseen view prediction via GANs but it is not limited to other parts, and compare the resulting performance with [1] and existing work.

Reference:

[1] Tatarchenko M., Dosovitskiy A., Brox T. (2016) Multi-view 3D Models from Single Images with a Convolutional Network. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9911. Springer, Cham

[2] Code from [1], https://github.com/lmb-freiburg/mv3d

[3] Goodfellow, Ian, et al. “Generative adversarial nets.” *Advances in neural information processing systems*. 2014.

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