• 한국컴퓨터그래픽스학회논문지
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• 제21권5호
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• pp.37-44
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• 2015

본 논문에서는 전통적으로 삼차원 모델 복원에 사용되는 볼륨 기반 자료 구조의 한계점을 극복하기 위해 평면 해시 구조를 볼륨 구조와 상호보완적으로 사용하는 하이브리드 모델 표현을 제안한다. 실내 환경에 대한 삼차원 모델 복원은 좁은 공간에 대한 정밀한 복원 결과를 얻기 위해 볼륨 기반의 자료 구조를 사용하였으나, 이러한 볼륨 기반의 자료 구조는 메모리의 사용량이 많아 대규모 공간에 대한 삼차원 복원으로 확장이 용이하지 못하였다. 본 논문에서는 이러한 기존 삼차원 모델 복원의 확장성을 증가시키기 위해 메모리를 효율적으로 사용하는 평면 해시 모델 구조를 제안한다. 또한 이러한 제안된 평면 해시 모델 구조를 넓고 단순한 평면 복원을 위해 사요하고, 좁고 디테일한 공간 복원에는 기존 볼륨 구조를 동시에 사용하는 하이브리드 복원 방법을 사용한다. 제안된 기법은 GPU 상에서 구현되어 공간을 실시간으로 복원 가능하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3782-3796
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• 2020

A three-dimensional (3D) reconstruction is an important research area in computer vision. The ability to detect and match features across multiple views of a scene is a critical initial step. The tracking matrix W obtained from a 3D reconstruction can be applied to structure from motion (SFM) algorithms for 3D modeling. We often fail to generate an acceptable number of features when processing face or medical images because such images typically contain large homogeneous regions with minimal variation in intensity. In this study, we seek to locate sufficient matching points not only in general images but also in face and medical images, where it is difficult to determine the feature points. The algorithm is implemented on an adaptive threshold value, a scale invariant feature transform (SIFT), affine SIFT, speeded up robust features (SURF), and affine SURF. By applying the algorithm to face and general images and studying the geometric errors, we can achieve quasi-dense matching points that satisfy well-functioning geometric constraints. We also demonstrate a 3D reconstruction with a respectable performance by applying a column space fitting algorithm, which is an SFM algorithm.

• 한국측량학회지
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• 제25권6_1호
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• pp.545-555
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• 2007

3D buildings are the most prominent feature comprising urban scene. A few of mega-cities in the globe are virtually reconstructed in photo-realistic 3D models, which becomes accessible by the public through the state-of-the-art online mapping services. A lot of research efforts have been made to develop automatic reconstruction technique of large-scale 3D building models from remotely sensed data. However, existing methods still produce irregular building polygons due to errors induced partly by uncalibrated sensor system, scene complexity and partly inappropriate sensor resolution to observed object scales. Thus, a geometric regularization technique is urgently required to rectify such irregular building polygons that are quickly captured from low sensory data. This paper aims to develop a new method for regularizing noise building outlines extracted from airborne LiDAR data, and to evaluate its performance in comparison with existing methods. These include Douglas-Peucker's polyline simplication, total least-squared adjustment, model hypothesis-verification, and rule-based rectification. Based on Minimum Description Length (MDL) principal, a new objective function, Geometric Minimum Description Length (GMDL), to regularize geometric noises is introduced to enhance the repetition of identical line directionality, regular angle transition and to minimize the number of vertices used. After generating hypothetical regularized models, a global optimum of the geometric regularity is achieved by verifying the entire solution space. A comparative evaluation of the proposed geometric regulator is conducted using both simulated and real building vectors with various levels of noise. The results show that the GMDL outperforms the selected existing algorithms at the most of noise levels.