5 citations · 9 across the 3 of their papers we have counts for
7 papers
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
Feihu Zhang, Vladlen Koltun, Philip Torr +2
Semantic segmentation models struggle to generalize in the presence of domain shift. In this paper, we introduce contrastive learning for feature alignment in cross-domain adaptati…
Dancing under the stars: video denoising in starlight
Kristina Monakhova, Stephan R. Richter, Laura Waller +1
Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux…
Enhancing Photorealism Enhancement
Stephan R. Richter, Hassan Abu AlHaija, Vladlen Koltun
We present an approach to enhancing the realism of synthetic images. The images are enhanced by a convolutional network that leverages intermediate representations produced by conv…
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter +4
Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary…
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…
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
Stephan R. Richter, Stefan Roth
In this paper, we develop novel, efficient 2D encodings for 3D geometry, which enable reconstructing full 3D shapes from a single image at high resolution. The key idea is to pose…