4 citations · 6 across the 5 of their papers we have counts for
6 papers
Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling
Florian Piewak, Peter Pinggera, Marius Zöllner
State-of-the-art approaches for the semantic labeling of LiDAR point clouds heavily rely on the use of deep Convolutional Neural Networks (CNNs). However, transferring network arch…
Improved Semantic Stixels via Multimodal Sensor Fusion
Florian Piewak, Peter Pinggera, Markus Enzweiler +2
This paper presents a compact and accurate representation of 3D scenes that are observed by a LiDAR sensor and a monocular camera. The proposed method is based on the well-establis…
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation
Florian Piewak, Peter Pinggera, Manuel Schäfer +6
Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central compone…
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…