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Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
27.01.2023
Verantwortlich:
Huch, Sebastian
Autorinnen / Autoren:
Huch, Sebastian; Scalerandi, Luca; Rivera, Esteban; Lienkamp, Markus
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Titel:
S2R-DAD: Sim-to-Real Distribution-Aligned Dataset
Identifikator:
doi:10.14459/2023mp1695833
Enddatum der Datenerzeugung:
28.02.2022
Fachgebiet:
DAT Datenverarbeitung, Informatik; VER Technik der Verkehrsmittel
Quellen der Daten:
Experimente und Beobachtungen / experiments and observations; Simulationen / simulations
Datentyp:
Texte / texts; Datenbanken / data bases
Anderer Datentyp:
Point clouds and text files
Methode der Datenerhebung:
Real-world data generation using real-world LiDAR and GPS sensors. Simulated data generation using Unity Game Engine.
Beschreibung:
Sim-to-Real Distribution-Aligned Dataset (S2R-DAD) for Domain Shift and Domain Adaptation Analysis. Includes 12000 labeled point clouds in total, whereas 6000 are captured during the Indy Autonomous Challenge in Las Vegas in 2022. The other subset of 6000 samples is generated in simulation and includes the same scenarios, objects, and environment as the real counterpart. Each point cloud file contains the fused point clouds of three LiDAR sensors, covering 360deg horizontally in total. The lab...     »
Links:
This dataset relates to the publication: 10.1109/TIV.2023.3251650
Schlagworte:
Sim-to-Real; LiDAR; Point Cloud; Domain Shift; Domain Adaptation
Technische Hinweise:
View and download (11,8 GB total, 24007 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1695833):
rsync rsync://m1695833@dataserv.ub.tum.de/m1695833/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
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