Surveys on line drawing simplification for Chinese cultural computing

doi:10. 19682/ j. cnki. 1005-8885. 2022. 0014

中国邮电高校学报(英文版) ›› 2022, Vol. 29 ›› Issue (2): 33-42.doi: 10. 19682/ j. cnki. 1005-8885. 2022. 0014

所属专题：文化计算专题

• Special Topic: Cultural Computing • 上一篇下一篇

Surveys on line drawing simplification for Chinese cultural computing

陈佳舟¹,Amal Ahmed Hasan Mohammed¹,黄可妤¹,缪永伟²

1. 浙江工业大学
2. 杭州师范大学

收稿日期:2022-02-23 修回日期:2022-03-16 出版日期:2022-04-26 发布日期:2022-04-26
通讯作者: 陈佳舟 E-mail:cjz@zjut.edu.cn
基金资助:
国家自然科学基金;国家自然科学基金;浙江省文物保护科技项目

Surveys on line drawing simplification for Chinese cultural computing

Received:2022-02-23 Revised:2022-03-16 Online:2022-04-26 Published:2022-04-26
Supported by:
National Natural Science Foundation of China;National Natural Science Foundation of China

摘要/Abstract

摘要：

In the recent decade, many approaches of rough line drawing simplification were proposed, but they are not well summarized yet, especially from the perspective of Chinese cultural computing. In this paper, a comprehensive review of existing line drawing simplification methods was presented, including their algorithms, advantages/ disadvantages, inputs/ outputs, datasets and source codes, etc. For raster line drawings, related implification work was discussed according to four main categories: fitting-based methods, tracing-based methods, field-based methods, and learning-based methods. For vector line drawings, a deep investigation was introduced for two major steps of simplification: stroke grouping and stroke merging. Finally, conclusions were given, key challenges and future directions of line drawing simplification for Chinese traditional art were thoroughly discussed.

关键词: line drawing, sketch, simplification, cultural computing

Abstract:

Key words: line drawing, sketch, simplification, cultural computing

参考文献

[1] HILAIRE X, TOMBRE K. Robust and accurate vectorization of line drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(6): 890 -904.

[2] FAVREAU J D, LAFARGE F, BOUSSEAU A. Fidelity vs. simplicity: a global approach to line drawing vectorization. ACM Transactions on Graphics, 2016, 35(4): Article 120.

[3] NORIS G, HORNUNG A, SUMNER R W, et al. Topology driven vectorization of clean line drawings. ACM Transactions on Graphics, 2013, 32(1): Article 4.

[4] CHEN J Z, DU M Q, QIN X J, et al. An improved topology extraction approach for vectorization of sketchy line drawings. The Visual Computer, 2018, 34(12): 1633 -1644.

[5] NAJGEBAUER P, SCHERER R. Inertia-based fast vectorization of line drawings. Computer Graphics Forum, 2019, 38(7): 203 -213.

[6] CHEN J Z, LEI Q, MIAO Y W, et al. Vectorization of line drawing image based on junction analysis. Science China:

Information Sciences, 2015, 58(7): 99 -112.

[7] BESSMELTSEV M, SOLOMON J. Vectorization of line drawings via PolyVector fields. ACM Transactions on Graphics, 2019, 38: Article 9.

[8] STANKO T, BESSMELTSEV M, BOMMES D, et al. Integer-grid sketch simplification and vectorization. Computer Graphics Forum, 2020, 39(5): 149 -161.

[9] PUHACHOV I, NEVEU W, CHIEN E, et al. Keypoint-driven line drawing vectorization via PolyVector flow. ACM Transactions on Graphics, 2021, 40(6): Article 266.

[10] CHEN J Z, GUENNEBAUD G, BARLA P, et al. Non-oriented MLS gradient fields. Computer Graphics Forum, 2013, 32(8): 98 -109.

[11] CHEN X J, KANG S B, XU Y Q, et al. Sketching reality: realistic interpretation of architectural designs. ACM Transactions on Graphics, 2008, 27(2): Article 11.

[12] YAN C, VANDERHAEGHE D, GINGOLD Y. A benchmark for rough sketch cleanup. ACM Transactions on Graphics, 2020, 39(6): Article 163.

[13] SIMO-SERRA E, IIZUKA S, SASAKI K, et al. Learning to simplify: fully convolutional networks for rough sketch cleanup. ACM Transactions on Graphics, 2016, 35(4): Article 121.

[14] SASAKI K, IIZUKA S, SIMO-SERRA E, et al. Joint gap detection and inpainting of line drawings. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR’17 ), 2017, Jul 21 - 26, Honolulu, HI, USA.

Piscataway, NJ, USA: IEEE, 2017: 5768 -5776.

[15] SIMO-SERRA E, IIZUKA S, ISHIKAWA H. Mastering sketching: adversarial augmentation for structured prediction.

ACM Transactions on Graphics, 37(1): Article 11.

[16] XU X M, XIE M S, MIAO P Q, et al. Perceptual-aware sketch simplification based on integrated VGG layers. IEEE Transactions on Visualization and Computer Graphics, 2019, 27(1): 178 -189.

[17] GUO Y, ZHANG Z M, HAN C, et al. Deep line drawing vectorization via line subdivision and topology reconstruction. Computer Graphics Forum, 2019, 38(7): 81 -90.

[18] MO H R, SIMO-SERRA E, GAO C Y, et al. General virtual sketching framework for vector line art. ACM Transactions on Graphics, 2021, 40(4): Article 51

[19] EGIAZARIAN V, VOYNOV O, ARTEMOV A, et al. Deep vectorization of technical drawings. Proceedings of the 16th

European Conference on Computer Vision ( ECCV’20), 2020, Aug 23 - 28, Glasgow, UK. Berlin, Germany: Springer, 2020: 582 -598.

[20] SASAKI K, IIZUKA S, SIMO-SERRA E, et al. Learning to restore deteriorated line drawing. The Visual Computer, 2018, 34: 1077 -1085.

[21] GRYADITSKAYA Y, SYPESTEYN M, HOFTIJZER J W, et al. OpenSketch: a richly-annotated dataset of product design sketches. ACM Transactions on Graphics, 2019, 38(6): Article 232.

[22] HA D, ECK D. A neural representation of sketch drawings. arXiv Preprint, arXiv: 1704. 03477, 2017.

[23] GE S, GOSWAMI V, ZITNICK C L, et al. Creative sketch generation. arXiv Preprint, arXiv: 2011. 10039, 2021.

[24] CHAN C, AKLEMAN E, CHEN J N. Two methods for creating Chinese painting. Proceedings of the 10th Pacific Conference on Computer Graphics and Applications, 2002, Oct 9 -11, Beijing, China. Piscataway, NJ, USA: IEEE, 2002: 403 -412.

[25] BARLA P, THOLLOT J, SILLION F X. Geometric clustering for line drawing simplification. Proceedings of the 16th Eurographics Conference on Rendering Techniques (EGSR’05), 2005, Jun 29- Jul 1, Konstanz, Germany. Aire-la-Ville, Switzerland: Eurographics Association, 2005: 183 -192.

[26] SHESH A, CHEN B Q. Efficient and dynamic simplification of line drawings. Computer Graphics Forum, 2008, 27(2): 537 -545.

[27] LIU X T, WONG T T, HENG P A. Closure-aware sketch simplification. ACM Transactions on Graphics, 2015, 34 (6):

Article 168.

[28] LIU C X, ROSALES E, SHEFFER A. StrokeAggregator: consolidating raw sketches into artist-intended curve drawings. ACM Transactions on Graphics, 37(4): Article 97.

[29] LIU Y, LI X M, BO P B, et al. Sketch simplification guided by complex agglomeration. Science China: Information Sciences, 2019, 62(5): Article 52105.

[30] VAN MOSSEL D P, LIU C X, VINING N, et al. Strokestrip: Joint parameterization and fitting of stroke clusters. ACM

Transactions on Graphics, 2021, 40(4): Article 50.

Surveys on line drawing simplification for Chinese cultural computing

Surveys on line drawing simplification for Chinese cultural computing

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价