Examination About Influence for Precision of 3d Image Measurement From the Ground Control Point Measurement and Surface Matching

Anai, Tetsuji; Kochi, Nobuo; Yamada, Mitsuharu; Sasaki, Takeshi; O-Tani, Hideyuki; Sasaki, David; Nishimura, S; Kimoto, Keisuke; Yasui, N.

doi:10.5194/isprsarchives-xl-4-w5-165-2015

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…https://geospatial.trimble.com/products-andsolutions/trimble-inpho-uasmaster (Abdel-Wahab, Wenzel, & Fritsch, 2012;Hugenholtz et al, 2013) iWitnessPRO-V4 ✓ Solución completa de procesamiento fotogramétrico (Photometrix) a partir de cámaras compactas de bajo costo desde tierra o plataformas RPAS. https://www.photometrix.com.au/iwitnesspro-v4/ (Fraser & Stamatopoulos, 2014) Topcon® Image MasterUAS ✓ Solución fotogrametría de la casa Topcon pensada para fotogrametría aérea tripulada pero optimizada para imágenes RPAS y de objeto cercano (Terrestre) https://www.topconpositioning.com/programasolutions/mass-data-mapping/magnet-collage (Anai et al, 2012(Anai et al, , 2015…”

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Contribuciones metodológicas para la obtención de información altimétrica requerida en la evaluación local de amenazas por inundaciones a partir de nuevas tecnologías geoespaciales

Villanueva¹

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Contribuciones metodológicas para la obtención de información altimétrica requerida en la evaluación local de amenazas por inundaciones a partir de nuevas tecnologías geoespaciales

Villanueva¹

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MMM and CLCFM: A 3D point cloud reconstruction method based on photogrammetry for all-around images taken by rotating an individual plant

Hayashi,

Kochi,

Kodama

et al. 2024

Preprint

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This research proposes a novel technique for acquiring a large amount of high-density, high-precision 3D point cloud data for plants. We propose two methods, multi-masked matching (MMM) and the closed-loop coarse-to-fine method (CLCFM). The proposed approach addresses challenges in reconstructing plant 3D point clouds from all-around images using SfM and multi-view stereo methods. Given the complex structure of plants, with thin objects like leaves and stems overlapping, reconstructing accurate 3D point clouds presents significant challenges. One of these is occlusion, where points in the 3D point cloud cannot be obtained due to overlapping parts, preventing accurate point capture. Another is the generation of erroneous points in non-existent locations due to image matching errors along object outlines. The MMM mitigates these problems through an iterative process that alternates between locally generating point clouds and refining these point clouds by removing erroneous points using mask images. The proposed pipeline enables the construction of plant 3D point clouds with minimal noise and fewer missing points. By facilitating the acquisition of high-density, high-precision 3D data on a large number of plant bodies, as required for research activities, the proposed method is expected to enable comparative analysis of visible phenotypes in the growth process of a wide range of plant species based on 3D information.

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A 3D Shape-Measuring System for Assessing Strawberry Fruits

Kochi¹,

Tanabata²,

Hayashi³

et al. 2018

IJAT

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Plant shape measurements have conventionally been conducted in plant science by classifying their shape features, by measuring their widths and lengths with a Vernier caliper, or by similar methods. Those measurements rely heavily on human senses and manual labor, making it difficult to acquire massive data. Additionally, they are prone to large measurement differences. To cope with those problems of conventional measuring methods, we are developing a three-dimensional (3D) shape-measuring system using images and a reliable assessment technique. 3D objects enable us to assess and measure shape features with high accuracy and to automatically measure volume, which conventional methods cannot. Thus, our new system is capable of automatically and efficiently measuring objects. Our goal is to obtain wide acceptance of our method at actual research sites. Unlike industrial products, it is difficult to properly assess the measurements of plants because of their object fluctuations and shape complexities. This paper describes our automatic 3D shape-measuring system, the method for assessing measurement accuracy, and the assessment results. The measurement accuracy of the developed system for strawberry fruits is 0.6 mm or less for 90% or more of the fruit and 0.3 mm or less for 80%. This evidence supports the system’s capability of shape assessment. The developed system can fully automate photographing, measuring, and modeling objects and can semi-automatically analyze them, reducing the time required for the entire process from the conventional time of 6–7 h to 1.5 h. The developed system is designed for users with no technical knowledge so that they can easily use it to acquire 3D measurement data on plants. Thus, we intend to expand measurable objects from strawberry fruits to other plants and their parts, including leaves, stalks, and flowers

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Examination About Influence for Precision of 3d Image Measurement From the Ground Control Point Measurement and Surface Matching

Cited by 4 publications

References 6 publications

Contribuciones metodológicas para la obtención de información altimétrica requerida en la evaluación local de amenazas por inundaciones a partir de nuevas tecnologías geoespaciales

Contribuciones metodológicas para la obtención de información altimétrica requerida en la evaluación local de amenazas por inundaciones a partir de nuevas tecnologías geoespaciales

MMM and CLCFM: A 3D point cloud reconstruction method based on photogrammetry for all-around images taken by rotating an individual plant

A 3D Shape-Measuring System for Assessing Strawberry Fruits

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