Critical analysis of instruments and measurement techniques of the shape of trees: Terresrial Laser scanner and Structured Light scanner

Xhimitiku, Iva; Rossi, Gianluca; Baldoni, Luciana; Marsili, Roberto; Coricelli, Marco

doi:10.1109/metroagrifor.2019.8909215

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…The performances of these shape measurement techniques have been assessed reconstructing the torso of two adults (one male and one female); the main objective of this first analysis was to evaluate the performances of two low-cost tools [16], [24], [25] (smartphone camera and iPad Pro LiDAR), in relation to the accurate reconstruction obtainable with the structured light scanner, used as reference measurement system [26]. Once the performances of these tools have been defined under 'ideal' scanning conditions (collaborative subject able to maintain a position throughout the scan process), the same techniques have been used to obtain a set of 3D scans of a 4-year-old boy's torso, at an orthopaedic laboratory (Officina Ortopedica Semidoro srl, Perugia, ITALY).…”

Section: Introductionmentioning

confidence: 99%

3D shape measurement techniques for human body reconstruction

Xhimitiku¹,

Pascoletti

Zanetti

et al. 2022

ACTA IMEKO

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<p>In this work the performances of three different techniques for 3D scanning have been investigated. In particular two commercial tools (smartphone camera and iPad Pro LiDAR) and a structured light scanner (Go!SCAN 50) have been used for the analysis. First of all, two different subjects have been scanned with the three different techniques and the obtained 3D model were analysed in order to evaluate the respective reconstruction accuracy. A case study involving a child was then considered, with the main aim of providing useful information on performances of scanning techniques for clinical applications, where boundary conditions are often challenging (i.e., non-collaborative patient). Finally, a full procedure for the 3D reconstruction of a human shape is proposed, in order to setup a helpful workflow for clinical applications.</p>

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Section: Introductionmentioning

confidence: 99%

3D shape measurement techniques for human body reconstruction

Xhimitiku¹,

Pascoletti

Zanetti

et al. 2022

ACTA IMEKO

Self Cite

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“…Although this method can be inexpensive and frees the user from the need of stereocalibrate the cameras, it requires a large number of photographs taken from different angles, making it unsuitable for timevarying displacement measurements. In order to simplify the experimental setup and lower costs, a structured light projection technology (Xhimitiku et al, 2019:) was used to perform the 3D shape reconstruction of the 4DP WPC sample. The light projected onto the object is reflected from its surface, then captured by the camera array.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Depth-Camera: Open-Source 3d Displacement Measurements for 4d Printed Hygroscopic Composites

Bianconi

Filippucci

Pelliccia

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 4D printing (4DP) is a growing branch of 3D printing technology that involves the design of composite material architectures capable of shape-change transformations, which occur post printing, in response to external stimulus. Among these, Wood Polymer Composites (WPCs) change their shape in reaction to changes of moisture content, shrinking or swelling like natural wood until the equilibrium with the environment is reached. Such intrinsic material behavior can be particularly useful in the development of passive moisture airflow controllers that can modulate humidity and airflow in indoor environments to improve air quality. Precise measurement of the time-based stimulus induced shape-change response of these composites is critical to assess the responsiveness, velocity of reaction and overall deformation of the designed 4DP composite mechanisms. Up until now, Digital Image Correlation (DIC) techniques have been widely used for such purpose. However, DIC methods require expensive equipment and costly commercial software. This paper presents a Low-Cost Depth-Camera (LCDC) method that uses a free custom algorithm that returns a 3D coloured displacement map with the corresponding meshes of the acquired object. The LCDC method does not require specialized equipment and allows for an overall understanding of the time-dependent deformation of 4DP actuators, this method also facilitates the comparison between composites with different properties under the same external conditions. This new LCDC method has the potential to further 4DP research by providing an open-source, accessible and reliable tool to assess 3D displacement measurements.

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“…A fixed camera is used a reference and its performance is compared with those of three scanning techniques, namely structured light scanner (SL), LIDAR (L), photogrammetry with high resolution camera (Pc), and an autonomous, low-cost photogrammetry system (Pl). The performances in reproducing the plant shape are assessed with the purpose of developing and standardizing a low-cost procedure to monitor the crop growth, saving time and resources [7,8]. Indeed, while 2D measurement performed by a fixed camera allows for a real-time monitoring thanks to sub-hourly image acquisition, on the other hand it would probably not be a feasible solution in a greenhouse, especially in vertical farms.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

Xhimitiku

Bianchi

Proietti

et al. 2021

2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

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This paper presents a comparison of different methodologies for monitoring the plants growth in a greenhouse. A 2D measurement based on Computer Vision algorithms and 3D shape measurements techniques (Structured light, LIDAR and photogrammetry) are compared. From the joined 2D and 3D data, an analysis was performed considering health plant indicators. The methodologies are compared among each other. The acquired data are then fed into Deep Learning algorithms in order to detect anomalies in plant growth. The final aim is to give an assessment on the image acquisition methodologies, selecting the most suitable to be used to create the Deep Learning model inputs saving time and resources.

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Critical analysis of instruments and measurement techniques of the shape of trees: Terresrial Laser scanner and Structured Light scanner

Cited by 3 publications

References 4 publications

3D shape measurement techniques for human body reconstruction

3D shape measurement techniques for human body reconstruction

Low-Cost Depth-Camera: Open-Source 3d Displacement Measurements for 4d Printed Hygroscopic Composites

Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

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