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Reducing the Amount of Real World Data for Object Detector Training with
  Synthetic Data

Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data

31 January 2022
Sven Burdorf
Karoline Plum
Daniel Hasenklever
ArXivPDFHTML

Papers citing "Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data"

2 / 2 papers shown
Title
Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine
  Learning in Epigraphy
Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy
Andrei C. Aioanei
R. Hunziker-Rodewald
Konstantin Klein
Dominik L. Michels
33
2
0
11 Oct 2023
Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation
  Using Object Detectors and Analyzing Point Clouds at Target-Level
Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level
Sebastian Huch
Luca Scalerandi
Esteban Rivera
Markus Lienkamp
3DPC
29
16
0
03 Mar 2023
1