4.3k citations · 19.2k across the 101 of their papers we have counts for
9 papers · 2 filters
Consensus Maximization Tree Search Revisited
Zhipeng Cai, Tat-Jun Chin, Vladlen Koltun
Consensus maximization is widely used for robust fitting in computer vision. However, solving it exactly, i.e., finding the globally optimal solution, is intractable. A* tree searc…
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
René Ranftl, Katrin Lasinger, David Hafner +2
The success of monocular depth estimation relies on large and diverse training sets. Due to the challenges associated with acquiring dense ground-truth depth across different envir…
High Speed and High Dynamic Range Video with an Event Camera
Henri Rebecq, René Ranftl, Vladlen Koltun +1
Event cameras are novel sensors that report brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with…
Zoom To Learn, Learn To Zoom
Xuaner Cecilia Zhang, Qifeng Chen, Ren Ng +1
This paper shows that when applying machine learning to digital zoom for photography, it is beneficial to use real, RAW sensor data for training. Existing learning-based super-reso…
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…
Does computer vision matter for action?
Brady Zhou, Philipp Krähenbühl, Vladlen Koltun
Computer vision produces representations of scene content. Much computer vision research is predicated on the assumption that these intermediate representations are useful for acti…