• ISSN 0258-2724
  • CN 51-1277/U
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Volume 30 Issue 1
Jan.  2017
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Journal of Southwest Jiaotong University  > 2017  >  30(1): 164-170.
TIAN Huaiwen, GUO Shizhang, YANG Ningxue. Algorithm for Reconstructing Polyhedral Objects from Single Axonometric Line Drawing Based on Rotation Transformation[J]. Journal of Southwest Jiaotong University, 2017, 30(1): 164-170. doi: 10.3969/j.issn.0258-2724.2017.01.023
Citation: TIAN Huaiwen, GUO Shizhang, YANG Ningxue. Algorithm for Reconstructing Polyhedral Objects from Single Axonometric Line Drawing Based on Rotation Transformation[J]. Journal of Southwest Jiaotong University, 2017, 30(1): 164-170. doi: 10.3969/j.issn.0258-2724.2017.01.023
shu

Algorithm for Reconstructing Polyhedral Objects from Single Axonometric Line Drawing Based on Rotation Transformation

doi: 10.3969/j.issn.0258-2724.2017.01.023
  • Received Date: 24 May 2015
  • Publish Date: 25 Feb 2017
    Abstract
  • The divide-and-conquer strategy is adopted to improve the efficiency of 3D reconstruction of a single line drawing and guarantee the convergence of the optimization algorithm for polyhedral objects. First, the single line drawing is divided into a lot of closed polygons, then all closed polygons are grouped according to their connection relationships, and finally all faces represented by the closed polygons in one group are reconstructed into 3D. In this way, a normal global optimization problem is resolved in a step-by-step fashion. Based on this strategy, several local optimization criteria are discussed, and a step resolving algorithm for 3D reconstruction by use of the rotation transformation are proposed. By rotating the child face against its parent face until some local optimization criteria are met, all child faces in the same level can be resolved in 3D space. Utilizing the top-down linkage relationships among faces represented by the loops on the drawing, all the child faces will be reconstructed step by step. Case studies indicate that this method is suitable for 3D reconstruction of multi-face objects and the efficiency can be improved by 20% compared with typical global optimization methods.

     

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