• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1448-1450
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• 2015

본 논문은 3D 스캐너 및 센서 등으로 캡처되어 3D로 복원된 얼굴 객체의 부위별 의미 있는 영역에 대한 분할을 자동으로 수행하는 기술을 제안한다. 3D 스캔된 얼굴 모델을 모델링, 애니메이션, 3D 프린팅 등의 다양한 응용분야에 활용하기 위해서는 스캔된 영역의 의미 있는 부위별 인식이 필수적이다. 본 논문에서는 부위별 의미 있는 영역 레이블링이 된 템플릿 모델을 입력된 3D 복원 모델로 전이하여 복원된 3D 모델의 부위별 의미 있는 영역을 자동으로 분할하고 분할된 영역의 일관성을 유지하는 알고리즘을 제안한다.

• Journal of the Korea Computer Graphics Society
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• v.23 no.1
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• pp.57-65
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• 2017

In this paper, we present a dictionary learning method for reducing errors in point cloud models and reconstructing their geometry. For this, 3D feature information is extracted from the models which have a similar shape characteristic as the target model. Then a dictionary is constructed and the geometry is reconstructed using the dictionary. The presented method in this paper consists of the following three steps. First, a geometric patch is constructed from a similar model. Second, a morphological 3D feature of the acquired patch is learned. Third, a geometry reconstruction is performed using the learned dictionary. Finally, the error between the original model and the reconstruction result is calculated, and the accuracy of the reconstruction result is checked.

• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.135-142
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• 2003

Robust extraction of 3D object's features, shape and global motion information from 2D image sequence is described. The object's 21 feature points on the pyramid type synthetic object are extracted automatically using color transform technique. The extracted features are used to recover the 3D shape and global motion of the object using stereo paraperspective camera model and sequential SVD(Singuiar Value Decomposition) factorization method. An inherent error of depth recovery due to the paraperspective camera model was removed by using the stereo image analysis. A 30 synthetic object with 21 features reflecting various position was designed and tested to show the performance of proposed algorithm by comparing the recovered shape and motion data with the measured values.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.27-33
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• 2003

The image-based 3D modeling is the technique of generating a 3D graphic model from images acquired using cameras. It is being researched as an alternative technique for the expensive 3D scanner. In this paper, we propose the image-based, 3D modeling system using calibrated stereo cameras. The proposed algorithm for rendering, 3D model consists of three steps, camera calibration, 3D reconstruction, and 3D registration step. In the camera calibration step, we estimate the camera matrix for the image aquisition camera. In the 3D reconstruction step, we calculate 3D coordinates using triangulation from corresponding points of the stereo image. In the 3D registration step, we estimate the transformation matrix that transforms individually reconstructed 3D coordinates to the reference coordinate to render the single 3D model. As shown the result, we generated relatively accurate 3D model.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.436-443
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• 1999

Most of the 3D object reconstruction techniques divide the object into multiplane and approximate the surfaces of the object. The Marching Cubes Algorithm which initializes the mesh structure using a given isovalue. and Delaunay Tetrahedrisation are widely used. Deformable models are well-suited for general object reconstruction because they make little assumptions about the shape to recover and they can reconstruct objects *om various types of datasets. Now, many researchers are studying the reconstruction systems based on a deformable model. In this paper, we propose a novel method for reconstruction of 3D objects. This method, for a 3D object composed of curved planes, compresses the 3D object based on the adaptive simplexmesh technique. It changes the pre-defined mesh structure, so that it may approach to the original object. Also, we redefine the geometric characteristics such as curvatures. As results of simulations, we show reconstruction of the original object with high compression and concentration of vertices towards parts of high curvature in order to optimize the shape description.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.389-390
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• 2018

본 논문에서는 3차원 복원을 위한 특징점 추출 및 매칭에 대한 보다 정확한 방법을 제안한다. 이 방법은 컴퓨터 비전의 기본이 되는 분야로 복원뿐 만 아니라 SLAM과 같은 지도 작성 및 자율 운행에도 필요한 방법이다. 본 연구는 3차원 물체 복원을 위해서 사용하는 방법 중 하나인 Column space fitting(CSF)을 이용하여 turntable-image data에 적용하여 성능을 평가하여 정확성을 검증을 한다. 오늘날 3D scanner를 이용하여 물체를 3차원 모델을 획득하고 3D프린터를 이용하여 다양한 분야에 적용한다. 그러나 고가의 장비이기 때문에 접근성이 떨어진다. 본 연구는 영상들만을 가지고 기하학적 계산을 통해 3차원 모델을 획득한다. 본 연구결과는 기존의 방법인 KLT 알고리즘과 비교하여 RMSE의 값을 약 5배를 줄이는 성능 향상을 보인다.

• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.65-74
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• 2021

3D reconstruction means that reconstructing the 3D shape of the object in an image and a video. We proposed a progressive occupancy network architecture that can recover not only the overall shape of the object but also the local details. Unlike the original occupancy network, which uses a feature vector embedding information of the whole image, we extract and utilize the different levels of image features depending on the receptive field size. We also propose a novel network architecture that applies the image features sequentially to the decoder blocks in the decoder and improves the quality of the reconstructed 3D shape progressively. In addition, we design a novel decoder block structure that combines the different levels of image features properly and uses them for updating the input point feature. We trained our progressive occupancy network with ShapeNet. We compare its representation power with two prior methods, including prior occupancy network(ONet) and the recent work(DISN) that used different levels of image features like ours. From the perspective of evaluation metrics, our network shows better performance than ONet for all the metrics, and it achieved a little better or a compatible score with DISN. For visualization results, we found that our method successfully reconstructs the local details that ONet misses. Also, compare with DISN that fails to reconstruct the thin parts or occluded parts of the object, our progressive occupancy network successfully catches the parts. These results validate the usefulness of the proposed network architecture.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.26-34
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• 2008

The information of traversability and path planning is essential for UGV(Unmanned Ground Vehicle) navigation. Such information can be obtained by analyzing 3D terrain. In this paper, we present the method of 3D terrain modeling with color information from a camera, precise distance information from a 2D Laser Range Finder(LRF) and wheel encoder information from mobile robot with less data. And also we present the method of 3B terrain modeling with the information from GPS/IMU and 2D LRF with less data. To fuse the color information from camera and distance information from 2D LRF, we obtain extrinsic parameters between a camera and LRF using planar pattern. We set up such a fused system on a mobile robot and make an experiment on indoor environment. And we make an experiment on outdoor environment to reconstruction 3D terrain with 2D LRF and GPS/IMU(Inertial Measurement Unit). The obtained 3D terrain model is based on points and requires large amount of data. To reduce the amount of data, we use cubic grid-based model instead of point-based model.

• Smart Media Journal
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• v.11 no.9
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• pp.75-80
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• 2022

Recently, research and development on immersive virtual reality(VR) technology to provide a realistic experience is being widely conducted. To provide realistic experience in immersive virtual reality for VR participants, virtual environments should consist of high-realistic environments using 3D reconstruction. In this paper, to acquire 3D information in real space using multiple cameras in the reconstruction process, we propose a novel method of optimal camera placement for accurate reconstruction to minimize distortion of 3D information. Through our approach in this paper, real 3D information can obtain with minimized errors during environment reconstruction, and it is possible to provide a more immersive experience with the created virtual environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.150-161
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• 2013

This paper presents a new system which produces high resolution 3D contents by capturing multiview images of an object using multiple cameras, and estimating geometric and texture information of the object from the captured images. Even though a variety of multiview image-based 3D reconstruction systems have been proposed, it was difficult to generate high resolution 3D contents because multiview image-based 3D reconstruction requires a large amount of memory and computation. In order to reduce computational complexity and memory size for 3D reconstruction, the proposed system predetermines the regions in input images where an object can exist to extract object boundaries fast. And for fast computation of a visual hull, the system represents silhouettes and 3D-2D projection/back-projection relations by chain codes and 1D homographies, respectively. The geometric data of the reconstructed object is compactly represented by a 3D segment-based data format which is called DoCube, and the 3D object is finally reconstructed after 3D mesh generation and texture mapping are performed. Experimental results show that the proposed system produces 3D object contents of $800{\times}800{\times}800$ resolution with a rate of 2.2 seconds per frame.