• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.184-187
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• 2002

Two-dimensional (2-D) X-ray computerized tomography (CT) equipments are widely used in industrial and medical fields, and nowadays studies on reconstruction algorithm for 3-D cone-beam acquisition systems are active for better utilization. The authors recent-By have proposed a fast reconstruction aigorithm using tree-structured filter bank for 2-D C1, and shown the algorithm is applicable to an approximate reconstruction of 3-D CT. For exact 3-D CT reconstruction, however, we have to backproject 1-D signal into 3-D space. This paper proposes a fast implementation method for this back-projection by use of tree-structured filter bank. and shows the proposed method works approximately 700 times faster than the direct one with almost same reconstruction image quality.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.1-6
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• 2009

In order to obtain a 3-D urban model, an abstraction of the surface model is required. This paper describes works on the 3D reconstruction and modeling by the grouping 3D line segments extracted from the stereo matching of edges, which is derived from multiple images. The grouping is achieved by conditions of degrees and distances between lines. Building objects are determined by the junction combinations of the grouped line segments. The proposed algorithm demonstrates effective results of 3D reconstruction of buildings with 2D aerial images.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.438-444
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• 2018

Structured light vision system has been widely used in 3D surface profiling. Usually, it is composed of a camera and a laser which projects a line on the target. Calibration is necessary to acquire 3D information using structured light stripe vision system. Conventional calibration algorithms have found the pose of the camera and the equation of the stripe plane of the laser under the same coordinate system of the camera. Therefore, the 3D reconstruction is only possible under the camera frame. In most cases, this is sufficient to fulfill given tasks. However, they require multiple images which are acquired under different poses for calibration. In this paper, we propose a calibration algorithm that could work by using just one shot. Also, proposed algorithm could give 3D reconstruction under both the camera and laser frame. This would be done by using newly designed calibration structure which has multiple vertical planes on the ground plane. The ability to have 3D reconstruction under both the camera and laser frame would give more flexibility for its applications. Also, proposed algorithm gives an improvement in the accuracy of 3D reconstruction.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.78-82
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• 2007

This paper propose a building reconstruction method of urban area for a 3D GIS with stereo images. The 3D reconstruction is performed by the grouping 3D line segments extracted from the stereo matching of salient edges which are derived from multiple images. The grouping is achieved by conditions of degrees and distances between lines. Building objects are determined by the junction combinations of the grouped line segments. The proposed algorithm demonstrates effective results of 3D reconstruction of buildings with 2D aerial images.

• ETRI Journal
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• v.42 no.3
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• pp.420-432
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• 2020

The reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition and computer vision. Therefore, we implement an algorithm with the help of a 3D model to perform reconstruction from the real datasets using the slope features. This approach avoids the problem of gaps created through the loss of parts of the artifacts. Therefore, the aim of this study is to assemble the object without previous knowledge about the form of the original object. We utilize the edges of the fragments as an important feature in reconstructing the objects and apply multiple procedures to extract the 3D edge points. In order to assign the positions of the unknown parts that are supposed to match, the contour must be divided into four parts. Furthermore, to classify the fragments under reconstruction, we apply a backpropagation neural network. We test the algorithm on several models of ceramic fragments. It achieves highly accurate results in reconstructing the objects into their original forms, in spite of absent pieces.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2034-2042
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• 2006

In this study, a complete 3D surface reconstruction method is proposed based on the concept that the vertices, of surface model can be completely matched to the unstructured point cloud. In order to generate the initial mesh model from the point cloud, the mesh subdivision of bounding box and shrink-wrapping algorithm are introduced. The control mesh model for well representing the topology of point cloud is derived from the initial mesh model by using the mesh simplification technique based on the original QEM algorithm, and the parametric surface model for approximately representing the geometry of point cloud is derived by applying the local subdivision surface fitting scheme on the control mesh model. And, to reconstruct the complete matching surface model, the insertion of isolated points on the parametric surface model and the mesh optimization are carried out. Especially, the fast 3D surface reconstruction is realized by introducing the voxel-based nearest-point search algorithm, and the simulation results reveal the availability of the proposed surface reconstruction method.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.173-180
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• 2005

This paper proposes an effective algorithm that combines both the stereo matching and the marching cube algorithm. By applying the stereo matching technique to an image obtained from various angles, 3D geometry data are acquired, and using the camera extrinsic parameter, the images are combined. After reconstructing the combined data into mesh using the image index, the normal vector equivalent to each point is obtained and the mesh smoothing is processed. This paper describes the successive processes and techniques on the 3D mesh reconstruction, and by proposing the intermediate iso- surface algorithm. Therefore it improves the 3D data instability problem caused when using the conventional marching cube algorithm.

• Proceedings of the KIEE Conference
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• 1979.08a
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• pp.158-159
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• 1979

Based on minimum-mean-sqare error criterion, a noise filtering algorithm for the reconstruction of an image function from noisy projection data is suggested. The filter is constructed with a few projection data. This algorithm requires less computational time compared with other noise filtering algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2414-2433
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• 2019

The aim of this paper is to design a 3D laser radar prototype based on laser triangulation. The mathematical model of distance sensitivity is deduced; a pixel-distance conversion formula is discussed and used to complete 3D scanning. The center position extraction algorithm of the spot is proposed, and the error of the linear laser, camera distortion and installation are corrected by using the proposed weighted average algorithm. Finally, the three-dimensional analytic computational algorithm is given to transform the measured distance into point cloud data. The experimental results show that this 3D laser radar can accomplish the 3D object scanning and the environment 3D reconstruction task. In addition, the experiment result proves that the product of the camera focal length and the baseline length is the key factor to influence measurement accuracy.