Primitive Shape Fitting in Point Clouds Using the Bees Algorithm

Baronti, Luca; Alston, Mark; Mavrakis, Nikos; Esfahani, Amir Masoud Ghalamzan; Castellani, Marco

doi:10.3390/app9235198

Cited by 11 publications

(9 citation statements)

References 26 publications

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“…The problem of representing the surface of a point cloud can be explored from an optimisation perspective. For instance, [2] uses the bees algorithm to fit primitive shapes to point clouds; similarly [6] solves the same problem by means of machine learning methods. In both cases, the resulting surfaces provide a compressed form of the data once described in terms of the primitive shapes.…”

Section: Other Approachesmentioning

confidence: 99%

Compressing Aerodynamic Hazard Data

Zhou

Boullé

Barton

et al. 2022

Preprint

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Data compression of three-dimensional computational fluid dynamics (CFD) simulation data is crucial to allow effective data-streaming for drone navigation and control. This problem is computationally challenging due to the complexity of the geometrical features present in the CFD data, and cannot be tackled by standard compression techniques such as sphere-tree. In this report, we present two different methods based on octree and cuboid primitives to compress velocity isosurfaces and volumetric data in three dimensions. Our volume compression method achieves a 1400 compression rate of raw simulation data and allows parallel computing.

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Section: Other Approachesmentioning

confidence: 99%

Compressing Aerodynamic Hazard Data

Zhou

Boullé

Barton

et al. 2022

Preprint

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“…The APS set was used to train the neural network classifiers. This model set was originally created by Baronti et al [45] for research on primitive shape fitting. The shape generation software can be downloaded from Baronti's GitHub repository 1 .…”

Section: The Artificial Primitive Shapes (Aps) Setsmentioning

confidence: 99%

“…The APS set contains point cloud models of the following three geometric shapes: box, cylinder, and sphere. Baronti et al [45] created 591 different artificial shapes by changing the height (H), width (W), breadth (B), and diameter (D) of the three geometric shapes. The APS set was created from a fullfactorial combination of the parameters defining each shape.…”

Section: The Artificial Primitive Shapes (Aps) Setsmentioning

confidence: 99%

Primitive shape recognition from real-life scenes using the PointNet deep neural network

Zheng

Castellani

2022

Int J Adv Manuf Technol

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In many industrial applications, it is possible to approximate the shape of mechanical parts with geometric primitives such as spheres, boxes, and cylinders. This information can be used to plan robotic grasping and manipulation procedures. The work presented in this paper investigated the use of the state-of-the-art PointNet deep neural network for primitive shape recognition in 3D scans of real-life objects. To obviate the need of collecting a large set of training models, it was decided to train PointNet using examples generated from artificial geometric models. The motivation of the study was the achievement of fully automated disassembly operations in remanufacturing applications. PointNet was chosen due to its suitability to process 3D models, and ability to recognise objects irrespective of their poses. The use of simpler shallow neural network procedures was also evaluated. Twenty-eight point cloud scenes of everyday objects selected from the popular Yale-CMU-Berkeley benchmark model set were used in the experiments. Experimental evidence showed that PointNet is able to generalise the knowledge gained on artificial shapes, to recognise shapes in ordinary objects with reasonable accuracy. However, the experiments showed some limitations in this ability of generalisation, in terms of average accuracy (78% circa) and consistency of the learning procedure. Using a feature extraction procedure, a multi-layer-perceptron architecture was able to achieve nearly 83% classification accuracy. A practical solution was proposed to improve PointNet generalisation capabilities: by training the neural network using an error-corrupted scene, its accuracy could be raised to nearly 86%, and the consistency of the learning results was visibly improved.

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“…Finally, the center is calculated from the reconstructed shape. The third step is to score the candidate's primitive shape (Equation ( 1)) [36]:…”

Section: Validationmentioning

confidence: 99%

Reliable Estimates of Merchantable Timber Volume from Terrestrial Laser Scanning

Panagiotidis

Abdollahnejad

2021

Remote Sensing

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Simple and accurate determination of merchantable tree height is needed for accurate estimations of merchantable volume. Conventional field methods of forest inventory can lead to biased estimates of tree height and diameter, especially in complex forest structures. Terrestrial laser scanner (TLS) data can be used to determine merchantable height and diameter at different heights with high accuracy and detail. This study focuses on the use of the random sampling consensus method (RANSAC) for generating the length and diameter of logs to estimate merchantable volume at the tree level using Huber’s formula. For this study, we used two plots; plot A contained deciduous trees and plot B consisted of conifers. Our results demonstrated that the TLS-based outputs for stem modelling using the RANSAC method performed very well with low bias (0.02 for deciduous and 0.01 for conifers) and a high degree of accuracy (97.73% for deciduous and 96.14% for conifers). We also found a high correlation between the proposed method and log length (−0.814 for plot A and −0.698 for plot B), which is an important finding because this information can be used to determine the optimum log properties required for analyzing stem curvature changes at different heights. Furthermore, the results of this study provide insight into the applicability and ergonomics during data collection from forest inventories solely from terrestrial laser scanning, thus reducing the need for field reference data.

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Primitive Shape Fitting in Point Clouds Using the Bees Algorithm

Cited by 11 publications

References 26 publications

Compressing Aerodynamic Hazard Data

Compressing Aerodynamic Hazard Data

Primitive shape recognition from real-life scenes using the PointNet deep neural network

Reliable Estimates of Merchantable Timber Volume from Terrestrial Laser Scanning

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