4.3k citations · 19.2k across the 101 of their papers we have counts for
14 papers · 1 filter
Learning by Cheating
Dian Chen, Brady Zhou, Vladlen Koltun +1
Vision-based urban driving is hard. The autonomous system needs to learn to perceive the world and act in it. We show that this challenging learning problem can be simplified by de…
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…
The Limited Multi-Label Projection Layer
Brandon Amos, Vladlen Koltun, J. Zico Kolter
We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi…
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…