Multi-view 3D models from single images with a convolutional network

192Citations
Citations of this article
307Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Tatarchenko, M., Dosovitskiy, A., & Brox, T. (2016). Multi-view 3D models from single images with a convolutional network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9911 LNCS, pp. 322–337). Springer Verlag. https://doi.org/10.1007/978-3-319-46478-7_20

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free