A Light-weight Approach for Automatically Reconstructing Large-scale Trees

DOI

10.2991/eeic-13.2013.17How to use a DOI?

Keywords

LiDAR point cloud; Tree modeling; Large-scale reconstruction; Texture completion

Abstract

Different from previous tree modeling approaches, our method is based on the idea of making tree reconstruction as quick as possible and simplifying the representation of final results while keeping the tree model visually acceptable. Each tree is represented by Billboard model. We first get the shape mask of a tree by projecting LiDAR point cloud onto 2D camera plane. Then we use the shape fitting method to obtain the corresponding rotation axes and bounding boxes for the main trunk and tree crown. We get the corresponding texture and correct the misalignment artifacts by texture completion. Finally, we rotate each textured polygon around the rotation axis to a certain degree. We demonstrate the effectiveness of our system with some LiDAR data sets and compare our tree modeling scheme with other state-of-the-art reconstruction algorithms to show its advantages in terms of speed and memory footprint.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wenmeng Zhou
AU  - Yao Yu
AU  - Yu Zhou
AU  - Sidan Du
PY  - 2013/12
DA  - 2013/12
TI  - A Light-weight Approach for Automatically Reconstructing Large-scale Trees
BT  - Proceedings of the 3rd International Conference on Electric and Electronics
PB  - Atlantis Press
SP  - 73
EP  - 78
SN  - 1951-6851
UR  - https://doi.org/10.2991/eeic-13.2013.17
DO  - 10.2991/eeic-13.2013.17
ID  - Zhou2013/12
ER  -