[1]李晨曦,孙正兴,宋沫飞,等.一种三维模型最优视图的分类选择方法[J].智能系统学报,2014,9(1):12-18.[doi:10.3969/j.issn.1673-4785.201305004]
LI Chenxi,SUN Zhengxing,SONG Mofei,et al.A classification-based approach for best view selection of 3D models[J].CAAI Transactions on Intelligent Systems,2014,9(1):12-18.[doi:10.3969/j.issn.1673-4785.201305004]
点击复制
LI Chenxi,SUN Zhengxing,SONG Mofei,et al.A classification-based approach for best view selection of 3D models[J].CAAI Transactions on Intelligent Systems,2014,9(1):12-18.[doi:10.3969/j.issn.1673-4785.201305004]
一种三维模型最优视图的分类选择方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第1期
页码:
12-18
栏目:
学术论文—智能系统
出版日期:
2014-02-25
- Title:
- A classification-based approach for best view selection of 3D models
- Keywords:
- 3D model; visualization; best view selection; view ranking; classification; classification-based approach
- 分类号:
- TP391
- 摘要:
- 为了三维模型数据集的可视化展示和缩略图的自动化生成, 提出了一种三维模型最优视图选择方法, 将最优视图选择归结为评价视图分类性能的问题。首先提取三维模型每个视图的特征, 然后在预先分好类的三维模型集上, 对每个视图训练分类器评价视图分类性能, 选择具有最佳分类性能的视图作为最优视图。实验结果表明, 选择出的视图符合人的视觉习惯, 而且相同类别的模型具有一致的最优视图, 证明了所提方法对于三维模型最优视图选择的有效性。
- Abstract:
- For automatic generation of the visualization and thumbnails of the 3D model set, an approach for best view selection of the 3D models is proposed, and this approach formulates the best view selection as a problem of evaluating views’ classification performance. Initially each of the 3D models’ view features is extracted, then for each view, we train the classifier that evaluates the view’s classification performance on a pre-classified set of 3D models. The view with the best classification performance is chosen as the best option. Finally, the selected views in the experiments are in accordance with the visual characteristics of humans, and the 3D models of the same class have the consistent best views. The results demonstrate the effectiveness of the approach for the best view selection.
- 参考文献/References:
-
[1] MARR D, POGGIO T, HILDRETH E C, et al. A computational theory of human stereo vision[M]. Boston: Birkh?use, 1991: 263-295.
[2] BLANZ V, TARR M J, BULTHOFF H H, et al. What object attributes determine canonical views?[J]. Perception, 1999, 28(5): 560-575.
[3] POLONSKY O, PATAN G, BIASOTTI S, et al. What’s in an image? Towards the computation of the "best" view of an object[J]. The Visual Computer, 2005, 21(8\\9\\10): 840-847.
[4] FEIXAS M, DEL ACEBO E, BEKAERT P, et al. An information theory framework for the analysis of scene complexity[J]. Computer Graphics Forum, 1999, 18(3): 95-106.
[5] 杨利明,王文成,吴恩化.基于视平面上特征计算的视点选择[J].计算机辅助设计与图形学学报, 2008, 20(9): 1097-1103.YANG Liming, WANG Wencheng, WU Enhua. Viewpoint selection by feature measurement on the viewing plane[J]. Journal of Computer-Aided Design & Computer Graphics, 2008, 20(9): 1097-1103.
[6] 曹伟国,胡平,李华,等.基于距离直方图的最优视点选择[J].计算机辅助设计与图形学学报, 2010, 22(9): 1515-1521.CAO Weiguo, HU Ping, LI Hua, et al. Canonical viewpoint selection based on distance-histogram[J]. Journal of Computer-Aided Design & Computer Graphics, 2010, 22(9): 1515-1521.
[7] JOSHUA P, PHILIP S, ALEKSEY G, et al. A planar-reflective symmetry transform for 3D shapes[J]. ACM Transactions on Graphics, 2006, 25(3): 549-559.
[8] YAMAUCHI H, SALEEM W, YOSHIZAWA S, et al. Towards stable and salient multi-view representation of 3D shapes[C]//Proceedings of the IEEE International Conference on Shape Modeling and Applications. Matsushima, Japan, 2006: 40.
[9] VáZQUEZ P P, FEIXAS M, SBERT M, et al. Automatic view selection using viewpoint entropy and its application to image-based modeling[J]. Computer Graphics Forum, 2003, 22(4): 689-700.
[10] SHILANE P, FUNKHOUSER T. Distinctive regions of 3D surfaces[J]. ACM Transactions on Graphics, 2007, 26(2): 7.
[11] VáZQUEZ P P. Automatic view selection through depth-based view stability analysis[J]. The Visual Computer, 2009, 25(5\\6\\7): 441-449.
[12] LAGA H. Data-driven approach for automatic orientation of 3D shapes[J]. The Visual Computer, 2011, 27(11): 977-989.
[13] FU H, COHEN-OR D, DROR G, et al. Upright orientation of man-made objects[J]. ACM Transactions on Graphics, 2008, 27(3): 1-7.
[14] VRANIC D V, SAUPE D. 3D model retrieval[C]//Proceedings of the Spring Conference on Computer Graphics and its Applications. Budmerice, Slovakia, 2000: 89-93.
[15] ZHANG D, LU G. An integrated approach to shape based image retrieval[C]//Proceedings of 5th Asian Conference on Computer Vision. Melbourne, Australia, 2002: 652-657.
[16] KAUPPIEN H, SEPANEN T. An experiment comparison of auto regressive and Fourier based descriptors in 2D shape classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2): 201-207.
[17] ZHANG D, LU G. Shape retrieval using Fourier descriptors[C]//Proceedings of 2nd IEEE Pacific Rim Conference on Multimedia. Beijing, China, 2001: 1-9.
[18] CHEN D Y, TIAN X P, SHEN Y T, et al. On visual similarity based 3D model retrieval[J]. Computer Graphics Forum, 2003, 22(3): 223-232.
[19] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45: 5-32.
[20] BOSCH A, ZISSERMAN A, MUNOZ X. Image classification using random forests and ferns[C]//Proceedings of 11th IEEE International Conference on Computer Vision. Rio de Janeiro, Brazil, 2007: 1-8.
[21] SHILANE P, MIN P, KAZHDAN M, et al. The Princeton shape benchmark[C]//Proceedings of the IEEE Shape Modeling International. Washington, DC, USA, 2004: 167-178.
- 相似文献/References:
-
[1]郑存芳,洪文学,李少雄,等.数据偏序结构关系中的知识发现可视化方法[J].智能系统学报,2016,11(4):475.[doi:10.11992/tis.201606019]
ZHENG Cunfang,HONG Wenxue,LI Shaoxiong,et al.A novel knowledge discovery visualization method based on data partial ordered structure[J].CAAI Transactions on Intelligent Systems,2016,11():475.[doi:10.11992/tis.201606019]
[2]邓廷权,王强.半监督类保持局部线性嵌入方法[J].智能系统学报,2021,16(1):98.[doi:10.11992/tis.202003007]
DENG Tingquan,WANG Qiang.Semi-supervised class preserving locally linear embedding[J].CAAI Transactions on Intelligent Systems,2021,16():98.[doi:10.11992/tis.202003007]
备注/Memo
收稿日期:2013-05-03。
基金项目:国家"863"计划资助项目(2007AA01Z334);国家自然科学基金资助项目(61272219,61100110,61021062);江苏省科技计划资助项目(BE2010072,BE2011058,BY2012190).
作者简介:李晨曦,男,1991年生,硕士研究生,主要研究方向为计算机图形学;宋沫飞,男,1986年生,博士研究生,主要研究方向为计算机图形学。
通讯作者:孙正兴,男,1964年生,教授、博士生导师、博士,中国计算机学会计算机辅助设计与图形学专委会委员,中国人工智能学会首届人工心理与人工情感专委会委员。主要研究方向为多媒体计算、计算机视觉与智能人机交互。先后主持和参与国家自然科学基金、国家"863"计划项目、企事业横向合作课题等项目多项,获省部级科技进步三等奖3次。发表学术论文近百篇,出版书籍5部.E-mail:szx@nju.edu.cn.
更新日期/Last Update:
1900-01-01