한국정보과학회논문지:시스템및이론 (Journal of KIISE:Computer Systems and Theory)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

Free-Hand 선화로부터 점진적 3차원 물체 복원

Progressive Reconstruction of 3D Objects from a Single Freehand Line Drawing

  • 오범수 (고려대학교 컴퓨터공학과) ;
  • 김창헌 (고려대학교 컴퓨터공학과)
  • 발행 : 2003.04.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 하나의 스케치 면도인 선화로부터 면 인식의 탐색 영역을 축소하고 다양한 3차원 물체를 빠르게 복원하는 점진적인 알고리즘을 제안한다. 복원 과정의 입력으로 사용되는 스케치 면도는 파선이 제거되지 않은 모서리-꼭지점 그래프인 2차원 스케치 면도로서 3차원 와이어프레임 물체의 부정확한 free-hand 스케치이다. 알고리즘은 두 단계로 수행된다. 면 인식 단계에서는 스케치 면도로부터 모든 가능 한 면을 생성하고 탐색 공간을 축소하기 위한 기하학적 위상학적 제약 조건을 이용하여 면을 불가능한 면, 기본 면, 최소 면으로 분류한다. 제안 알고리즘은 물체를 구성하는 실제 면을 빠르게 인식하기 위하여 최소 면만을 탐색한다 물체 생성 단계에서는 면의 스케치 순서에 따라 물체의 꼭지점 좌표를 최적화함으로써 3차원 구조를 점진적으로 계산한다. 점진적 방법은 복원 과정에서 물체와 스케치 도면 사이의 관계로부터 유도된 3차원 제약 조건을 적용함으로써 최적 3차원 물체를 빠르게 복원한다. 또한, 스케치 도중에 시점 이동을 허용한다. 점진적 복원 알고리즘을 기술하고 실제 구현 결과를 보인다.

This paper presents a progressive algorithm that not only can narrow down the search domain in the course of face identification but also can fast reconstruct various 3D objects from a sketch drawing. The sketch drawing, edge-vertex graph without hidden line removal, which serves as input for reconstruction process, is obtained from an inaccurate freehand sketch of a 3D wireframe object. The algorithm is executed in two stages. In the face identification stage, we generate and classify potential faces into implausible, basis, and minimal faces by using geometrical and topological constraints to reduce search space. The proposed algorithm searches the space of minimal faces only to identify actual faces of an object fast. In the object reconstruction stage, we progressively calculate a 3D structure by optimizing the coordinates of vertices of an object according to the sketch order of faces. The progressive method reconstructs the most plausible 3D object quickly by applying 3D constraints that are derived from the relationship between the object and the sketch drawing in the optimization process. Furthermore, it allows the designer to change viewpoint during sketching. The progressive reconstruction algorithm is discussed, and examples from a working implementation are given.

키워드

참고문헌

  1. M. Shpitalni and H. Lipson. Classification of Sketch Strokes and Corner Detection Using Conic Sections and Adaptive Clustering. Trans. of the ASME. J. of Mechanical Design, 119(2), 1997
  2. B. S. Oh and C. H. Kim. Fast Reconstruction of 3D Objects from Single Free-Hand Line Drawing. LNCS, 2059:706-715, 2001
  3. M. Shpitalni and H. Lipson. Identification of Faces in a 2D Line Drawing Projection of a Wireframe Object. IEEE Trans. Pattern Analysis & Machine Intell., 18(10):1000-1012, 1996 https://doi.org/10.1109/34.541409
  4. H. Lipson and M. Shpitalni. Optimization Based Reconstruction of a 3D Object From a Single Freehand Line Drawing. Computer Aided Design, 28(8):651-663, 1996 https://doi.org/10.1016/0010-4485(95)00081-X
  5. B.S. Oh and C. H. Kim. Progressive 3D Reconstruction from a Sketch Drawing. In 9th Pacific Graphics, pp. 108-117, 2001 https://doi.org/10.1109/PCCGA.2001.962863
  6. D. A. Huffman. Impossible Objects as Nonsence Sentences. Machine Intelligence, Edinbrugh University Press, pp. 295-323, 1971
  7. T. Kanade. Recovery of the Three-Dimensional Shape of an Object from a Single View. Artificial Intelligence, 17:409-460, 1980 https://doi.org/10.1016/0004-3702(81)90031-X
  8. J. Malik. Interpreting Line Drawing of Curved Object. Int. J. of Computer Vision, 1:73-103, 1987 https://doi.org/10.1007/BF00128527
  9. E. Marti, J. Regomcos, J. Lopez-Krahe, and J. J. Villanueva. Hand Line Drawing Interpretation as Three-Dimensional Objects. Signal Processing, 32:91-110, 1993 https://doi.org/10.1016/0165-1684(93)90038-C
  10. A. K. Mackworth. Interpreting Pictures of Polyhedral Scenes. Artificial Intelligence, 4:121-137, 1973 https://doi.org/10.1016/0004-3702(73)90003-9
  11. I. J. Grimstead and R. R. Martin. Creating Solid Models from single 2D Sketches. Solid Modeling'95, pp. 323-337, 1995 https://doi.org/10.1145/218013.218082
  12. K. Sugihara. Interpretation of Line Drawing, The MIT Press, 1986
  13. Y. Fukui. Input Method of Boundary Solid by Sketching. Computer Aided Design, 20(8):434-440, 1988 https://doi.org/10.1016/0010-4485(88)90001-2
  14. D. Lamb and A. Bandopadhay. Interpreting a 3D Object from a Rough 2D Line Drawing. Proceeding of Visualization 90, pp. 59-66, 1990 https://doi.org/10.1109/VISUAL.1990.146365
  15. D. F. Rogers and J. A. Adams. Mathematical Elements for Computer Graphics, McDraw-Hill, 1976
  16. L. Braid and P. Wang. Three-Dimensional Object Recognition Using Gradient Descent and the Universal Three-Dimensional Array Grammar. SPIE, 1607:711-718, 1991 https://doi.org/10.1117/12.57106
  17. Y. G. Leclerc and M. A. Fiscler. An Optimization Based Approach to the Interpretation of Single Line Drawings as 3D Wire Frames. Int. J. of Computer Vision, 9(2):113-136, 1992 https://doi.org/10.1007/BF00129683
  18. T. MarilI. Emulating the Human Interpretation of Line Drawings as Three-Dimensional Objects. Int. J. of Computer Vision, 6(2):147-161, 1991 https://doi.org/10.1007/BF00128154
  19. W. Wang and G. Grinstein. A Survey of 3D Solid Reconstruction from 2D Projection Line Drawings. Computer Graphics Forum, 12:137-158, 1993 https://doi.org/10.1111/1467-8659.1220137
  20. H. Lipson. Computer Aided 3D Sketching for Conceptual Design. PhD Thesis, Israel Institute of Technology, Israel, 1998
  21. R. P. Brent. Algorithms for Minimization without Derivatives. Prentice Hall, Englewood Cliffs N. J.,, Chap. 7. 1973
  22. W. H. Press, S. A. Teukolsky, W. T. VetterJing, and B. P. Flannery. Numerical Recipes in C: The Art of Scientific Computing (2nd ed.). Cambridge University Press, Cambridge, 1992