Proximity-Aware Multiple Meshes Decimation using Quadric Error Metric

Proximity-Aware Multiple Meshes Decimation using Quadric Error Metric

Anahid Ghazanfarpour, Nicolas Mellado, Chems-Eddine Himeur, Loïc Barthe, Jean-Pierre Jessel
CNRS, IRIT, Université de Toulouse, France.

Graphical Models, May 2020


Progressive mesh decimation by successive edge collapses is a standard tool in geometry processing. A key element of such algorithms is the error metric, which prioritizes the edge collapses to greedily minimize the simplification error. Most previous works focus on preserving local shape properties. However, meshes describing complex systems often require significant decimation for fast transmission and visualization on low-end terminals, and preserving the arrangement of objects is required to maintain the overall system readability for applications such as on-site repair, inspection, training, serious games, etc. We present a novel approach for the joint decimation of multiple triangular meshes. We combine local edge error (e.g. Quadric Error Metric) with a proximity-aware penalty function, which increases the error of edge collapses modifying the geometry in proximity areas. We propose an automatic detection of proximity areas and we demonstrate the performances of our approach on several models generated from CAD scenes.


Left: Car scene with 425 meshes and 3M faces in total. Right: Result of our proximity-aware decimation to 150k faces in total. Some meshes are rendered with transparent material to better observe the scene complexity

Bibtex

@article{GHAZANFARPOUR2020101062,
title = {Proximity-aware multiple meshes decimation using quadric error metric},
journal = {Graphical Models},
volume = {109},
pages = {101062},
year = {2020},
issn = {1524-0703},
doi = {https://doi.org/10.1016/j.gmod.2020.101062},
url = {https://www.sciencedirect.com/science/article/pii/S1524070320300059},
author = {Anahid Ghazanfarpour and Nicolas Mellado and Chems E. Himeur and Loïc Barthe and Jean-Pierre Jessel},
keywords = {Mesh decimation, Quadric error metric, Geometry processing, Virtual disassembly},
}
SLAM-aided forest plots mapping combining terrestrial and mobile laser scanning

SLAM-aided forest plots mapping combining terrestrial and mobile laser scanning

Jie Shao (1,2), Wuming Zhang (3,4), Nicolas Mellado (2), Nan Wang (5), Shuangna Jin (1), Shangshu Cai(1), Lei Luo (6), Thibault Lejemble (2), Guangjian Yan (1).
(1) State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China
(2) IRIT, CNRS, University of Toulouse, France
(3) School of Geospatial Engineering and Science, Sun Yat-Sen University, China
(4) Southern Marine Science and Engineering Guangdong Laboratory, China
(5) School of Remote Sensing and Information Engineering, Wuhan University, China

(6) Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, China

ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 163, May 2020


Precise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy.

Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions.

Bibtex

@article{SHAO2020214,
title = {SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {163},
pages = {214-230},
year = {2020},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2020.03.008},
url = {https://www.sciencedirect.com/science/article/pii/S0924271620300782},
author = {Jie Shao and Wuming Zhang and Nicolas Mellado and Nan Wang and Shuangna Jin and Shangshu Cai and Lei Luo and Thibault Lejemble and Guangjian Yan},
keywords = {Forest mapping, LiDAR, SLAM, Single-scan TLS, MLS},
}