Search Results for author: Frederik Hagelskjaer

Found 4 papers, 2 papers with code

ParaPose: Parameter and Domain Randomization Optimization for Pose Estimation using Synthetic Data

no code implementations2 Mar 2022 Frederik Hagelskjaer, Anders Glent Buch

The use of synthetic training data avoids this data collection problem, but a configuration of the training procedure is necessary to overcome the domain gap problem.

Pose Estimation

Deep learning classification of large-scale point clouds: A case study on cuneiform tablets

2 code implementations22 Feb 2022 Frederik Hagelskjaer

This paper introduces a novel network architecture for the classification of large-scale point clouds.


Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization

no code implementations17 Nov 2020 Frederik Hagelskjaer, Anders Glent Buch

While the use of synthetic training data prevents the need for manual annotation, there is currently a large performance gap between methods trained on real and synthetic data.

Data Augmentation Pose Estimation

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