28 citations · 32 across the 4 of their papers we have counts for
5 papers · 1 filter
Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors
Jan Bechtold, Maxim Tatarchenko, Volker Fischer +1
Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches predominantly rely on…
Parting with Illusions about Deep Active Learning
Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek +1
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. Rece…
Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
Sudhanshu Mittal, Maxim Tatarchenko, Thomas Brox
The ability to understand visual information from limited labeled data is an important aspect of machine learning. While image-level classification has been extensively studied in…
What Do Single-view 3D Reconstruction Networks Learn?
Maxim Tatarchenko, Stephan R. Richter, René Ranftl +3
Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research. All existing techniques are united by…
Tangent Convolutions for Dense Prediction in 3D
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun +1
We present an approach to semantic scene analysis using deep convolutional networks. Our approach is based on tangent convolutions - a new construction for convolutional networks o…