Výsledky vyhledávání - "Florian Piewak"

CNN-Based Lidar Point Cloud De-Noising in Adverse Weather

Autor: Philipp Schindler, Florian Piewak, Robin Heinzler, Wilhelm Stork

Publikováno v: IEEE Robotics and Automation Letters. 5:2514-2521

Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidar-based scene u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb552c8c31d479f1e6929b13e2e9d018
https://doi.org/10.1109/lra.2020.2972865

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Boosting LiDAR-Based Semantic Labeling by Cross-modal Training Data Generation

Autor: Markus Enzweiler, David Pfeiffer, Manuel Schäfer, Nick Schneider, J. Marius Zollner, Beate Schwarz, David Peter, Florian Piewak, Peter Pinggera

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030110239
ECCV Workshops (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 component of state-of-the-art perception systems. In addition to accurate spa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::27fb8767f70443c2af6ae1253af7f03d
https://doi.org/10.1007/978-3-030-11024-6_39

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Improved Semantic Stixels via Multimodal Sensor Fusion

Autor: Markus Enzweiler, Florian Piewak, Peter Pinggera, Marius Zöllner, David Pfeiffer

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030129385
GCPR

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-established Stixel model originally developed for stereo vision applications.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1a7948ed554fc691a20392159a6349e2
https://doi.org/10.1007/978-3-030-12939-2_31

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Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling

Autor: Florian Piewak, Peter Pinggera, Marius Zöllner

Publikováno v: ITSC

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 architectures across different LiDAR sensor types represents a significant

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0008935391705d804b0de0df0c4f09c

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Fully convolutional neural networks for dynamic object detection in grid maps

Autor: Michael Weber, Timo Rehfeld, J. Marius Zollner, Florian Piewak

Publikováno v: Intelligent Vehicles Symposium

Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convoluti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37635646f1ea146125448d5a573b0c1e
https://doi.org/10.1109/ivs.2017.7995750

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RegNet: Multimodal Sensor Registration Using Deep Neural Networks

Autor: Florian Piewak, Christoph Stiller, Uwe Franke, Nick Schneider

Publikováno v: Intelligent Vehicles Symposium

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, exemplified using a scanning LiDAR and a monocular camera. Compared to existin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::751f395ede6600cf261251e6ea3a4a17

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