VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

Abhishek Sharma, Oliver Grau, Mario Fritz

ECCV 2016 pp. 236-250

doi:10.1007/978-3-319-49409-8_20 /eccv/2016/sharma2016eccv-vconv/

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 (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes.

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Cite

Text

Sharma et al. "VConv-DAE: Deep Volumetric Shape Learning Without Object Labels." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-49409-8_20

Markdown

[Sharma et al. "VConv-DAE: Deep Volumetric Shape Learning Without Object Labels." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/sharma2016eccv-vconv/) doi:10.1007/978-3-319-49409-8_20

BibTeX

@inproceedings{sharma2016eccv-vconv,
  title     = {{VConv-DAE: Deep Volumetric Shape Learning Without Object Labels}},
  author    = {Sharma, Abhishek and Grau, Oliver and Fritz, Mario},
  booktitle = {European Conference on Computer Vision},
  year      = {2016},
  pages     = {236-250},
  doi       = {10.1007/978-3-319-49409-8_20},
  url       = {https://mlanthology.org/eccv/2016/sharma2016eccv-vconv/}
}