Towards Real-time Object Recognition and Pose Estimation in Point Clouds

Marlon Marcon, Olga Bellon, Luciano Silva

Abstract

Object recognition and 6DoF pose estimation are quite challenging tasks in computer vision applications. Despite efficiency in such tasks, standard methods deliver far from real-time processing rates. This paper presents a novel pipeline to estimate a fine 6DoF pose of objects, applied to realistic scenarios in real-time. We split our proposal into three main parts. Firstly, a Color feature classification leverages the use of pre-trained CNN color features trained on the ImageNet for object detection. A Feature-based registration module conducts a coarse pose estimation, and finally, a Fine-adjustment step performs an ICP-based dense registration. Our proposal achieves, in the best case, an accuracy performance of almost 83% on the RGB-D Scenes dataset. Regarding processing time, the object detection task is done at a frame processing rate up to 90 FPS, and the pose estimation at almost 14 FPS in a full execution strategy. We discuss that due to the proposal’s modularity, we could let the full execution occurs only when necessary and perform a scheduled execution that unlocks real-time processing, even for multitask situations.

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Paper Citation


in Harvard Style

Marcon M., Bellon O. and Silva L. (2021). Towards Real-time Object Recognition and Pose Estimation in Point Clouds.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 164-174. DOI: 10.5220/0010265601640174


in Bibtex Style

@conference{visapp21,
author={Marlon Marcon and Olga Bellon and Luciano Silva},
title={Towards Real-time Object Recognition and Pose Estimation in Point Clouds},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={164-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010265601640174},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Towards Real-time Object Recognition and Pose Estimation in Point Clouds
SN - 978-989-758-488-6
AU - Marcon M.
AU - Bellon O.
AU - Silva L.
PY - 2021
SP - 164
EP - 174
DO - 10.5220/0010265601640174