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Simulation-to-Reality Domain Adaptation for Offline 3D Object Annotation on Pointclouds with Correlation Alignment

Topics: 3D Vision Analyses ; Computer Vision Algorithms; Deep Learning and Neural Networks; Image Analysis and Scene Understanding; Motion, Tracking and 3D Vision; Pattern Recognition; Perception Engineering; Simulation and Software Tools; Traffic Control and Autonomous Vehicles

Authors: Weishuang Zhang ; B. Ravi Kiran ; Thomas Gauthier ; Yanis Mazouz and Theo Steger

Affiliation: Navya, France

Keyword(s): Pointclouds, Object Detection, 3D, Simulation, Unsupervised Domain Adaptation.

Abstract: Annotating objects with 3D bounding boxes in LiDAR pointclouds is a costly human driven process in an autonomous driving perception system. In this paper, we present a method to semi-automatically annotate real-world pointclouds collected by deployment vehicles using simulated data. We train a 3D object detector model on labeled simulated data from CARLA jointly with real world pointclouds from our target vehicle. The supervised object detection loss is augmented with a CORAL loss term to reduce the distance between labeled simulated and unlabeled real pointcloud feature representations. The goal here is to learn representations that are invariant to simulated (labeled) and real-world (unlabeled) target domains. We also provide an updated survey on domain adaptation methods for pointclouds.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Zhang, W., Kiran, B. R., Gauthier, T., Mazouz, Y. and Steger, T. (2022). Simulation-to-Reality Domain Adaptation for Offline 3D Object Annotation on Pointclouds with Correlation Alignment. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE; ISBN 978-989-758-563-0; ISSN 2795-4943, SciTePress, pages 142-149. DOI: 10.5220/0011059200003209

@conference{improve22,
author={Weishuang Zhang and B. Ravi Kiran and Thomas Gauthier and Yanis Mazouz and Theo Steger},
title={Simulation-to-Reality Domain Adaptation for Offline 3D Object Annotation on Pointclouds with Correlation Alignment},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE},
year={2022},
pages={142-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011059200003209},
isbn={978-989-758-563-0},
issn={2795-4943},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE
TI - Simulation-to-Reality Domain Adaptation for Offline 3D Object Annotation on Pointclouds with Correlation Alignment
SN - 978-989-758-563-0
IS - 2795-4943
AU - Zhang, W.
AU - Kiran, B.
AU - Gauthier, T.
AU - Mazouz, Y.
AU - Steger, T.
PY - 2022
SP - 142
EP - 149
DO - 10.5220/0011059200003209
PB - SciTePress