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Conference Paper

VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons180916

Sharma,  Abhishek
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Citation

Sharma, A., Grau, O., & Fritz, M. (2016). VConv-DAE: Deep Volumetric Shape Learning Without Object Labels. In G. Hua, & H. Jégou (Eds.), Computer Vision - ECCV 2016 Workshops (pp. 236-250). Berlin: Springer. doi:10.1007/978-3-319-49409-8_20.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-0642-6
Abstract
With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (eg. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Furthermore, deep learning research argues ~\cite{Vincent08} that learning representation with over-complete model are more prone to overfitting compared to the approach that learns from noisy data. Thus, we investigate a full convolutional volumetric denoising auto encoder that is trained in a unsupervised fashion. It outperforms prior work on recognition as well as more challenging tasks like denoising and shape completion. In addition, our approach is atleast two order of magnitude faster at test time and thus, provides a path to scaling up 3D deep learning.