Multi-view 3D Models from Single Images with a Convolutional Network View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016-09-16

AUTHORS

Maxim Tatarchenko , Alexey Dosovitskiy , Thomas Brox

ABSTRACT

We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object. Concretely, the network can predict an RGB image and a depth map of the object as seen from an arbitrary view. Several of these depth maps fused together give a full point cloud of the object. The point cloud can in turn be transformed into a surface mesh. The network is trained on renderings of synthetic 3D models of cars and chairs. It successfully deals with objects on cluttered background and generates reasonable predictions for real images of cars. More... »

PAGES

322-337

Book

TITLE

Computer Vision – ECCV 2016

ISBN

978-3-319-46477-0
978-3-319-46478-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46478-7_20

DOI

http://dx.doi.org/10.1007/978-3-319-46478-7_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014977512


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