Search Results for author: Maxim Tatarchenko

Found 9 papers, 5 papers with code

Parting with Illusions about Deep Active Learning

no code implementations11 Dec 2019 Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek, Thomas Brox

Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples.

Active Learning Data Augmentation +2

What Do Single-view 3D Reconstruction Networks Learn?

no code implementations CVPR 2019 Maxim Tatarchenko, Stephan R. Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, Thomas Brox

Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research.

3D Reconstruction Classification +4

Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs

1 code implementation ICCV 2017 Maxim Tatarchenko, Alexey Dosovitskiy, Thomas Brox

We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation.

3D Reconstruction

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

no code implementations20 Nov 2015 Maxim Tatarchenko, Alexey Dosovitskiy, Thomas Brox

We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object.

Learning to Generate Chairs, Tables and Cars with Convolutional Networks

1 code implementation21 Nov 2014 Alexey Dosovitskiy, Jost Tobias Springenberg, Maxim Tatarchenko, Thomas Brox

We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color.

Cannot find the paper you are looking for? You can Submit a new open access paper.