264 citations · 433 across the 25 of their papers we have counts for
10 papers · 2 filters
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling
Sebastian Ramos, Stefan Gehrig, Peter Pinggera +2
The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system t…
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
Alexander Krull, Eric Brachmann, Sebastian Nowozin +3
State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational reso…
InstanceCut: from Edges to Instances with MultiCut
Alexander Kirillov, Evgeny Levinkov, Bjoern Andres +2
This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different…
DSAC - Differentiable RANSAC for Camera Localization
Eric Brachmann, Alexander Krull, Sebastian Nowozin +4
RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines ha…
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
Evgeny Levinkov, Jonas Uhrig, Siyu Tang +7
We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical a…
Can Ground Truth Label Propagation from Video help Semantic Segmentation?
Siva Karthik Mustikovela, Michael Ying Yang, Carsten Rother
For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves…