Extraction of tubular shapes from dense point clouds and application to tree reconstruction from laser scanned data

Ravaglia, Joris; Bac, Alexandra; Fournier, Richard

doi:10.1016/j.cag.2017.05.016

Cited by 14 publications

(10 citation statements)

References 27 publications

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“…This study aimed at expanding our current capabilities to estimate stem profile. We tested the STEP algorithm in natural forest environments and provided quantitative results on forest scenes instead of the visual assessments in [54]. Our results illustrated how the STEP algorithm is resilient to occlusion and noise in the point cloud.…”

Section: Discussionmentioning

confidence: 99%

“…The STEP algorithm was first introduced by Ravaglia et al [54]. It reconstructs tuboids that are defined as a series of circles with continuous locations, radii and orientations.…”

Section: Step Algorithmmentioning

confidence: 99%

“…where a ∈ [0, 1] is a balancing term, H m and H M are respectively the minimum and maximum scores of the accumulator space, and h m and h M are respectively the local minimum and maximum of the accumulator space around c(u). Further details on curve initialization and the optimization procedure that is used to reach the minimum energy are given in [54].…”

Section: Step Algorithmmentioning

confidence: 99%

“…In an attempt to overcome the limitations of TLS, we developed a new algorithm that is based upon a HT variant called STEP (Snake for Tuboid Extraction from Point cloud) [54]. The STEP algorithm was designed to estimate stem profile in natural forest environments.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we selected three stem profile estimation algorithms representing distinct strategies. We compared the reference measurements with the estimation results of (1) the STEP algorithm [54], (2) the advanced cylinder-fitting algorithm that was available in the CompuTree software platform [24], and (3) the SimpleTree QSM algorithm [59].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Ravaglia

Fournier

Bac

et al. 2019

Forests

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Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm.

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Section: Discussionmentioning

confidence: 99%

“…The STEP algorithm was first introduced by Ravaglia et al [54]. It reconstructs tuboids that are defined as a series of circles with continuous locations, radii and orientations.…”

Section: Step Algorithmmentioning

confidence: 99%

Section: Step Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Ravaglia

Fournier

Bac

et al. 2019

Forests

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Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees

2020

Self Cite

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Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process, we propose an innovative method designed to separate leaf points from wood points in terrestrial LiDAR point clouds of trees. Although simple, our approach learns trees characteristic point patterns efficiently and robustly. To train our 3D deep learning model, we constructed a 3D labeled point cloud data-set of different tree species. Experiments show that our 3D deep representation together with our geometric approach leads to significant improvement over the state-of-the-art methods in segmentation task.

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The model of feature extraction for free-form surface based on topological transformation

Kong

Zhang

2018

Applied Mathematical Modelling

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Extraction of tubular shapes from dense point clouds and application to tree reconstruction from laser scanned data

Cited by 14 publications

References 27 publications

Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees

The model of feature extraction for free-form surface based on topological transformation

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