Monocular Estimation of Translation, Pose and 3D Shape on Detected Objects using a Convolutional Autoencoder

Ivar Persson, Martin Ahrnbom, Mikael Nilsson

2022

Abstract

This paper present a 6DoF-positioning method and shape estimation method for cars from monocular images. We pre-learn principal components, using Principal Component Analysis (PCA), from the shape of cars and use a learnt encoder-decoder structure in order to position the cars and create binary masks of each camera instance. The proposed method is tailored towards usefulness for autonomous driving and traffic safety surveillance. The work introduces a novel encoder-decoder framework for this purpose, thus expanding and extending state-of-the-art models for the task. Quantitative and qualitative analysis is performed on the Apolloscape dataset, showing promising results, in particular regarding rotations and segmentation masks.

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Paper Citation


in Harvard Style

Persson I., Ahrnbom M. and Nilsson M. (2022). Monocular Estimation of Translation, Pose and 3D Shape on Detected Objects using a Convolutional Autoencoder. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 390-396. DOI: 10.5220/0010826600003124


in Bibtex Style

@conference{visapp22,
author={Ivar Persson and Martin Ahrnbom and Mikael Nilsson},
title={Monocular Estimation of Translation, Pose and 3D Shape on Detected Objects using a Convolutional Autoencoder},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={390-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010826600003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Monocular Estimation of Translation, Pose and 3D Shape on Detected Objects using a Convolutional Autoencoder
SN - 978-989-758-555-5
AU - Persson I.
AU - Ahrnbom M.
AU - Nilsson M.
PY - 2022
SP - 390
EP - 396
DO - 10.5220/0010826600003124