40 citations · 104 across the 10 of their papers we have counts for
11 papers
RealDiff: Real-world 3D Shape Completion using Self-Supervised Diffusion Models
Başak Melis Öcal, Maxim Tatarchenko, Sezer Karaoglu +1
Point cloud completion aims to recover the complete 3D shape of an object from partial observations. While approaches relying on synthetic shape priors achieved promising results i…
SceneTeller: Language-to-3D Scene Generation
Başak Melis Öcal, Maxim Tatarchenko, Sezer Karaoglu +1
Designing high-quality indoor 3D scenes is important in many practical applications, such as room planning or game development. Conventionally, this has been a time-consuming proce…
Histogram-based Deep Learning for Automotive Radar
Maxim Tatarchenko, Kilian Rambach
There are various automotive applications that rely on correctly interpreting point cloud data recorded with radar sensors. We present a deep learning approach for histogram-based…
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…
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics
Oier Mees, Maxim Tatarchenko, Thomas Brox +1
We present a convolutional neural network for joint 3D shape prediction and viewpoint estimation from a single input image. During training, our network gets the learning signal fr…