986 citations · 1.1k across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles
Peter Pinggera, Sebastian Ramos, Stefan Gehrig +3
Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on st…
Semantically Guided Depth Upsampling
Nick Schneider, Lukas Schneider, Peter Pinggera +3
We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only a…
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos +6
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, esp…
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
Jonas Uhrig, Marius Cordts, Uwe Franke +1
Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multi-task architectures or computationally expensive graphi…