### Abstract

The goal of sketch reconstruction is to take an inaccurate, 2D edge-vertex graph (i.e., sketch drawing) as input and reconstruct a 3D shape as output. However, traditional reconstruction methods based on image regularities tend to produce a distorted 3D shape. In part, this distortion is due to the inherent inaccuracies in the sketch, but it also relates to the failure to accurately distinguish between important and less important regularities. We propose a new self-correctional reconstruction algorithm that can progressively produce refined versions of sketch reconstructions. The algorithm corrects the shape and the drawing simultaneously using geometric error metrics. The proposed algorithm can minimize the distortion of the shape by adding 3D regularities to the image regularities. The self-correctional algorithm for minimizing the distortion of sketch reconstruction is discussed, and the experimental results show that the proposed method efficiently reconstructs more accurate 3D objects than previous ones.

Original language | English |
---|---|

Pages (from-to) | 528-538 |

Number of pages | 11 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 2669 |

Publication status | Published - 2003 Dec 1 |

### Fingerprint

### ASJC Scopus subject areas

- Biochemistry, Genetics and Molecular Biology(all)
- Computer Science(all)
- Theoretical Computer Science

### Cite this

**Self-correctional 3D shape reconstruction from a single freehand line drawing.** / Oh, BeomSoo; Kim, Chang-Hun.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Self-correctional 3D shape reconstruction from a single freehand line drawing

AU - Oh, BeomSoo

AU - Kim, Chang-Hun

PY - 2003/12/1

Y1 - 2003/12/1

N2 - The goal of sketch reconstruction is to take an inaccurate, 2D edge-vertex graph (i.e., sketch drawing) as input and reconstruct a 3D shape as output. However, traditional reconstruction methods based on image regularities tend to produce a distorted 3D shape. In part, this distortion is due to the inherent inaccuracies in the sketch, but it also relates to the failure to accurately distinguish between important and less important regularities. We propose a new self-correctional reconstruction algorithm that can progressively produce refined versions of sketch reconstructions. The algorithm corrects the shape and the drawing simultaneously using geometric error metrics. The proposed algorithm can minimize the distortion of the shape by adding 3D regularities to the image regularities. The self-correctional algorithm for minimizing the distortion of sketch reconstruction is discussed, and the experimental results show that the proposed method efficiently reconstructs more accurate 3D objects than previous ones.

AB - The goal of sketch reconstruction is to take an inaccurate, 2D edge-vertex graph (i.e., sketch drawing) as input and reconstruct a 3D shape as output. However, traditional reconstruction methods based on image regularities tend to produce a distorted 3D shape. In part, this distortion is due to the inherent inaccuracies in the sketch, but it also relates to the failure to accurately distinguish between important and less important regularities. We propose a new self-correctional reconstruction algorithm that can progressively produce refined versions of sketch reconstructions. The algorithm corrects the shape and the drawing simultaneously using geometric error metrics. The proposed algorithm can minimize the distortion of the shape by adding 3D regularities to the image regularities. The self-correctional algorithm for minimizing the distortion of sketch reconstruction is discussed, and the experimental results show that the proposed method efficiently reconstructs more accurate 3D objects than previous ones.

UR - http://www.scopus.com/inward/record.url?scp=35248878611&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35248878611&partnerID=8YFLogxK

M3 - Article

VL - 2669

SP - 528

EP - 538

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -