2 code implementations • 21 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.
no code implementations • 20 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.
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.
Ranked #3 on 3D Reconstruction on Data3D−R2N2
1 code implementation • CVPR 2018 • Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou
Our approach is based on tangent convolutions - a new construction for convolutional networks on 3D data.
Ranked #2 on Semantic Segmentation on S3DIS Area5 (Number of params metric)
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.
Ranked #1 on 3D Reconstruction on 300W
1 code implementation • 15 Aug 2019 • Sudhanshu Mittal, Maxim Tatarchenko, Thomas Brox
The ability to understand visual information from limited labeled data is an important aspect of machine learning.
1 code implementation • 17 Oct 2019 • Oier Mees, Maxim Tatarchenko, Thomas Brox, Wolfram Burgard
We present a convolutional neural network for joint 3D shape prediction and viewpoint estimation from a single input image.
3D Object Reconstruction From A Single Image 3D Reconstruction +3
no code implementations • 11 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.
no code implementations • CVPR 2021 • Jan Bechtold, Maxim Tatarchenko, Volker Fischer, Thomas Brox
Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training.
no code implementations • 6 Mar 2023 • Maxim Tatarchenko, Kilian Rambach
Compared to existing methods, the design of our approach is extremely simple: it boils down to computing a point cloud histogram and passing it through a multi-layer perceptron.