scholarly journals EVALUATION OF PHOTOGRAMMETRIC BLOCK ORIENTATION USING QUALITY DESCRIPTORS FROM STATISTICALLY FILTERED TIE POINTS

2018 ◽  
Vol XLII-2 ◽  
pp. 185-191 ◽  
Author(s):  
A. Calantropio ◽  
M. P. Deseilligny ◽  
F. Rinaudo ◽  
E. Rupnik
Keyword(s):  
Best Practices ◽  
Quality Assessment ◽  
Graphical User Interface ◽  
3D Model ◽  
Low Cost ◽  
Relative Orientation ◽  
Model Reconstruction ◽  
Future Directions ◽  
3D Model Reconstruction ◽  
Data Quality Assessment

Due to the increasing number of low-cost sensors, widely accessible on the market, and because of the supposed granted correctness of the semi-automatic workflow for 3D reconstruction, highly implemented in the recent commercial software, more and more users operate nowadays without following the rigorousness of classical photogrammetric methods. This behaviour often naively leads to 3D products that lacks metric quality assessment. This paper proposes and analyses an approach that gives the users the possibility to preserve the trustworthiness of the metric information inherent in the 3D model, without sacrificing the automation offered by modern photogrammetry software. At the beginning, the importance of Data Quality Assessment is outlined, together with some recall of photogrammetry best practices. With the purpose of guiding the user through a correct pipeline for a certified 3D model reconstruction, an operative workflow is proposed, focusing on the first part of the object reconstruction steps (tie-points extraction, camera calibration, and relative orientation). A new GUI (Graphical User Interface) developed for the open source MicMac suite is then presented, and a sample dataset is used for the evaluation of the photogrammetric block orientation using statistically obtained quality descriptors. The results and the future directions are then presented and discussed.

scholarly journals The model of direct relative orientation with seven constraints for geological landslides measurement and 3D reconstruction

2016 ◽  
Vol 20 (4) ◽  
pp. 1
Author(s):  
Yifu Chen ◽  
Zhong Xie ◽  
Zhenge Qiu ◽  
Min Zhang ◽  
Saishang Zhong
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
3D Model ◽  
Accuracy Assessment ◽  
Relative Orientation ◽  
Model Reconstruction ◽  
Orientation Model ◽  
3D Model Reconstruction ◽  
Least Squares Adjustment ◽  
The Impact

With the development of computer vision and high-precision 3D model reconstruction, used for the measurement and 3D reconstruction of the geological landslides, acquiring a high-precision relative orientation basing multiple images is crucial and the key point to ensuring and improving the accuracy of 3D model and space position. Currently, the conventional relative orientation model includes five independent parameters. For the linear relative orientation model, there are nine parameters to construct the linear space geometric relationship between the imaging and space point. To eliminate the impact of more parameterization and improve the accuracy and stability of solved parameters for the conventional direct relative orientation model, a new relative orientation model with seven constraints is proposed and validated in this paper. The additional constraints are derived from the orthogonal property of the rotation matrix of a stereo imaging pair and associated with the least squares adjustment to obtain a high-precision result of the relative orientation. Through the accuracy assessment using space position, it is revealed that the new proposed model is more advantage for the conventional type of direct relative orientation, especially at 3D model reconstruction and close range photogrammetric and applications for the geological landslides measurement. El modelo de orientación relativa directa con siete restricciones para la medida de deslizamientos de tierra y reconstrucción tridimensional ResumenCon el desarrollo del entorno computacional y la alta precisión del modelo de reconstrucción tridimensional, utilizados para la medida y reconstrucción de desprendimientos geológicos, es crucial la obtención de la orientación relativa de alta precisión basada en imágenes múltiples y es el punto clave para asegurar y mejorar la exactitud del modelo 3D y la posición espacial. Actualmente el modelo de orientación relativa incluye cinco parámetros independientes. En el modelo linear de orientación relativa hay nueve parámetros para construir la relación geométrica espacial linear entre el sondeo y la posición espacial. Para eliminar el impacto de más parametrización y mejorar la exactitud y la estabilidad de los parámetros resueltos el modelo de orientación relativa convencional, este artículo propone y valida un nuevo modelo de orientación relativa con siete restricciones. Las restricciones adicionales se derivan de la propiedad ortogonal de la matriz de rotación de la imagen estéreo y se asocian con el ajuste de los cuadrados mínimos para obtener un resultado de alta precisión de la orientación relativa. Al medir la exactitud con la posición espacial se revela que el nuevo modelo propuesto tiene más ventajas que aquel de orientación relativa directa, especialmente en el modelo de reconstrucción 3D y en las aplicaciones fotográmetricas de rango cercano para la evaluación de desprendimientos geológicos.

Sequential Depth Completion With Confidence Estimation for 3D Model Reconstruction

2021 ◽  
Vol 6 (2) ◽  
pp. 327-334
Author(s):  
Khang Truong Giang ◽  
Soohwan Song ◽  
Daekyum Kim ◽  
Sunghee Choi
Keyword(s):  
3D Model ◽  
Model Reconstruction ◽  
Confidence Estimation ◽  
3D Model Reconstruction

Development of AR-based scanning support system for 3D model reconstruction of work sites

2022 ◽  
pp. 1-15
Author(s):  
Yuki Harazono ◽  
Hirotake Ishii ◽  
Hiroshi Shimoda ◽  
Yasuyoshi Taruta ◽  
Yuya Kouda
Keyword(s):  
Support System ◽  
3D Model ◽  
Model Reconstruction ◽  
3D Model Reconstruction

3D Model Reconstruction from Multi-views of 2D Images using Radon Transform

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Siti Syazalina Mohd. Sobani ◽  
Nasrul Humaimi Mahmood ◽  
Nor Aini Zakaria ◽  
Ismail Ariffin
Keyword(s):  
Radon Transform ◽  
3D Model ◽  
Digital Camera ◽  
Computation Method ◽  
Model Reconstruction ◽  
Fixed Sequence ◽  
Model Object ◽  
3D Model Reconstruction ◽  
Images Processing ◽  
2D Images

This paper presents a simple computation method to reconstruct 3-dimensional (3D) model from a sequence of 2-dimensional (2D) images using a multiple-view camera setup. The 3D model is acquired by applying several images processing on few 2D images captured by digital camera with different angle of views. The setup for this study consisted of a digital camera mounted on a tripod stand focusing at a block of model object on a turntable with black floor and background. 36 different angles are used to capture the images where every view angle differs by ten degree (10°) with another view in a fixed sequence. The image processing applied on all 2D images to be reconstructed as 3D surface are image segmentation, Radon transform (RT), image filtering, morphological operation, edge detection, and boundary extraction. The results for 3D model reconstruction shows it is well reconstructed, with a smooth texture obtained using 3D mesh and Delaunay triangulation, while the shape is nearly identical to the original model while the remaining are distinguishable.  

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