• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2943-2948
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• 2015

Run 3D reconstruction using a single camera, based on the information, we are advancing research on driving assistance apparatus or can be informed how to pass the obstacle existing ahead the driver. As a result depth information falls but it is possible to provide information that can pass through an obstacle on the straight. For 3D reconstruction by measuring the internal parameters, it calculates the Fundamental matrix and matching to find the feature points obtained by executing the triangulation on the basis of this. When the through experiments try to confirm the results, the depth information is present error information in the X and Y axes which can determine whether or not to pass through an obstacle has reliability.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.817-818
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• 2008

In the present paper we propose a new method of intermediate view reconstruction between stereo images using belief propagation_based stereo matching. Intermediate view reconstruction is an important step for multiview 3D display. Many previous paper about intermediate view reconstruction using depth information to synthesize interview though stereo matching were proposed. But depth information is different to estimated accurately. In the present paper, in order to obtain accurate depth information, belief propagation_based stereo matching was used.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2950-2952
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• 1999

This paper presents the method of 3D reconstruction of the depth information from the endoscopic stereo scopic images. After camera modeling to find camera parameters, we performed feature-point based stereo matching to find depth information. Acquired some depth information is finally 3D reconstructed using the NURBS(Non Uniform Rational B-Spline) method and OpenGL. The final result image is helpful for the understanding of depth information visually.

• ETRI Journal
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• v.38 no.4
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• pp.599-605
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• 2016

For the implementation of a real-time holographic camera, fast and automatic holographic image reconstruction is an essential technology. In this paper, we propose a new automatic depth-detection algorithm for fast holography reconstruction, which is particularly useful for optical scanning holography. The proposed algorithm is based on the inherent phase difference information in the heterodyne signals, and operates without any additional optical or electrical components. An optical scanning holography setup was created using a heterodyne frequency of 4 MHz with a 500-mm distance and 5-mm depth resolution. The reconstruction processing time was measured to be 0.76 s, showing a 62% time reduction compared to a recent study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1690-1710
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• 2014

In this paper, we propose the rapid implementation of a 3-dimensional (3D) facial reconstruction from a single frontal face image and introduce a design for its application on a mobile device. The proposed system can effectively reconstruct human faces in 3D using an approach robust to lighting conditions, and a fast method based on a Canonical Correlation Analysis (CCA) algorithm to estimate the depth. The reconstruction system is built by first creating 3D facial mapping from a personal identity vector of a face image. This mapping is then applied to real-world images captured with a built-in camera on a mobile device to form the corresponding 3D depth information. Finally, the facial texture from the face image is extracted and added to the reconstruction results. Experiments with an Android phone show that the implementation of this system as an Android application performs well. The advantage of the proposed method is an easy 3D reconstruction of almost all facial images captured in the real world with a fast computation. This has been clearly demonstrated in the Android application, which requires only a short time to reconstruct the 3D depth map.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.1131-1134
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• 2008

In this invited paper, numerical reconstruction and pattern recognition using integral imaging are overviewed. The computational integral imaging method reconstructs three-dimensional information at arbitrary depth-levels. Photon-counting nonlinear matched filtering combined with the computational reconstruction provides promising results for the application of low-light level recognition.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.739-740
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• 2008

Method for 3D reconstruction from image points and geometric clues can be roughly classified as "model-based" and "constraint-based". We present a new method to reconstruct from one image a scene using depth-point. The our method is benchmarked synthetic data and its effectiveness is shown on photograph data.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.541-541
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• 2000

This paper Presents the method of 3D reconstruction of the depth information from the endoscopic stereo scopic images. After camera modeling to find camera parameters, we peformed feature-point based stereo matching to find depth information. Acquired some depth information is finally 3D reconstructed using the NURBS(Non Uniform Rational B-Spline) algorithm. The final result image is helpful for the understanding of depth information visually.

• ETRI Journal
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• v.44 no.6
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• pp.1034-1046
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• 2022

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.1
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• pp.21-28
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• 2005

In this paper, we studied resolution to the FBP(Filtered Back-Propagation) tomographic image reconstruction algorithms. In order to analyze the resolution to the tomographic images, we derived ambiguity function to this algorithm which can be reconstructed from the improved FBP image reconstruction algorithm by using fixed coordinate system practically. Through simulation using this function, we determined the lateral and depth resolution quantitively and then analyzed respectively. Simulation results show that the lateral and depth resolution to the improved FBP image reconstruction algerian was determined $0.27\lambda\;and\;0.70\lambda$ at the 3dB, and also $0.89\lambda\;and\;0.96\lambda$ at the 6dB respectively. This results proved that improved FBP reconstruction algorithms for diffraction tomography of incident planar wave is useful to developed the tomographic image system, analyze the resolution to the tomographic images, we derived ambiguity function to this algerian which can be reconstructed from the improved FBP image reconstruction algorithm by using fixed coordinate system.