986 citations · 1.1k across the 7 of their papers we have counts for
10 papers
Learning Stixel-based Instance Segmentation
Monty Santarossa, Lukas Schneider, Claudius Zelenka +3
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation. However, due to their sparse occurrence in t…
Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes
Nils Gählert, Niklas Hanselmann, Uwe Franke +1
Object detection is an important task in environment perception for autonomous driving. Modern 2D object detection frameworks such as Yolo, SSD or Faster R-CNN predict multiple bou…
Slanted Stixels: A way to represent steep streets
Daniel Hernandez-Juarez, Lukas Schneider, Pau Cebrian +6
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather re…
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
Nick Schneider, Florian Piewak, Christoph Stiller +1
In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplif…
The Stixel world: A medium-level representation of traffic scenes
Marius Cordts, Timo Rehfeld, Lukas Schneider +5
Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that inter…
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…