Research progress on key technologies of cultural heritage activation
- Vol. 27, Issue 6, Pages: 1988-2007(2022)
Published: 16 June 2022 ,
Accepted: 04 April 2022
DOI: 10.11834/jig.220198
移动端阅览
浏览全部资源
扫码关注微信
Published: 16 June 2022 ,
Accepted: 04 April 2022
移动端阅览
Guohua Geng, Xuelei He, Meili Wang, Kang Li, Xiaowei He. Research progress on key technologies of cultural heritage activation. [J]. Journal of Image and Graphics 27(6):1988-2007(2022)
中华民族文化资源丰富、种类繁多且艺术形式多样,源于民族,植根民间,承载着历史记忆,延续着文化血脉,是中华民族的根与魂。文化遗产分布广、流传年代长,具有多样性、独特性和不可再生性等特点,是研究古代人类文明发展的珍贵资料。目前随着信息技术的迅猛发展以及全球化的冲击,其传承形式发生了根本性变化,特别是非物质文化遗产的人际传承方式,因此亟待在资源与媒介、内容与技术之间搭建技术的桥梁,支撑中华文化遗产的数字化传承。本文基于先进的智能计算、数字媒体和虚拟现实/增强现实技术,结合文化遗产的传播过程和艺术特点,对文化遗产收集理解、虚实结合智能展示交互和智慧化平台建设等活化关键技术的发展现状、前沿动态、热点问题和发展趋势进行分析和综述。在文化遗产收集理解方面,针对复杂文物数字化所存在的瓶颈问题,介绍数字化采集与重建关键技术;介绍文化遗产元素、主题和风格等特征提取算法,分析文化遗产的构图特征、分布特征、色彩特征和造型特征等数字化模拟过程中的关键技术,介绍基于语义特征分析、理解和识别的文化遗产理解及建设关键技术。对比和分析传统图形图像处理和深度学习方法在民族文化数字化仿真过程的优缺点,比较算法特点和算法效率,阐述存在的问题和难点,并对民族文化数字化仿真进行展望。在虚实结合智能展示交互方面,针对文化遗产的实体化展示和虚拟化展示特点,结合前沿信息技术,以多模态图像处理、小样本学习以及风格化图像生成方法为技术手段,基于多源异构大数据分析、知识图谱及深度学习等研究,综述文化遗产数字化修复的关键技术。面向文化遗产3D交互展示的需求,结合文本、音频、视频、语义和故事检索,介绍基于内容的自然人机交互技术;通过实时逼真、虚实融合渲染以及基于增强现实技术介绍相应的数字展品虚拟交互展示新技术。在智慧化平台建设方面,针对目前文化遗产智慧化平台系统管理分散导致业务协同不畅、数据缺乏统一规范导致数据难以共享,系统维护难度大、管理成本高以及用户使用不便等诸多问题,介绍文化遗产大数据模型与私有云架构关键技术研究以及相关的智慧平台建设项目。通过以上文化遗产活化的关键技术主动融入国家发展重大战略,有利于挖掘和提升民族文化遗产保护传承技术,促进文化遗产活化技术的合理利用,扩大传播影响,对弘扬中华文明、促进文化繁荣、建设少数民族示范区以及构筑文化自信具有重要意义。
The Chinese nation culture
which is of abundant resources
various categories and art styles
origins from the Chinese nation and is deeply rooted in ordinary people
has kept a long record of the historical memory and continued the cultural vein. Indeed
the Chinese nation culture is the foundation and the soul of the Chinese nation. The cultural heritage
which has the trait of widespread dissemination
long history
various styles
unique form of expression and being nonrenewable
is the precious information for exploring the development of the ancient human civilization. Nowadays
with the rapid development of information technology and the widespread impact of globalization
the formation of cultural inheritance has changed fundamentally
especially in the way of interpersonal inheritance style of the non-material cultural heritage. Therefore
there is an urgent need to construct bridges between the resource and the media
the content and the technology in order to sustain the digital inheritance of the Chinese cultural heritage. This paper is organised based on the advanced intelligent computing
digital media and virtual reality/augmented reality technology
and combines the dissemination process and artistic characteristics of the cultural heritage. From the perspective of the development status
frontier trends
hot issues and development trends
the key activation technologies are analyzed and summarized in terms of the collection of cultural heritage
the combination of virtual and real intelligent display and interaction
and the construction of intelligent platforms. Firstly
in the aspect of the collection of cultural heritage
in the view of the bottleneck problems that exist in the digitization of complex cultural relics
the key technologies of digital collection and reconstruction are introduced; the feature extraction algorithms of cultural heritage elements
themes
styles
etc.
as well as the key technologies in the process of digital simulation to analyze the compositional features
distribution features
color features
and modeling features of cultural heritage are summarized; the key technologies of cultural heritage understanding and construction that are based on semantic feature analysis
understanding and recognition are introduced; the advantages and disadvantages of traditional image processing and deep learning methods in the process of digital simulation of national culture are analysed and discussed
and the characteristics and efficiency of the current algorithms are compared. Besides
the existing problems and difficulties are expounded
and an insight into the future of digital simulation of national culture is provided. Next
in the aspect of the combination of virtual and real intelligent display and interaction
for the characteristics of physical display and virtual display of cultural heritage
the key technologies of digital restoration of cultural heritage are reviewed. The important role of cutting-edge information technology including multi-modal image processing
few-shot learning and stylized image generation methods is introduced. Meanwhile
the cutting-edge research based on multi-source heterogeneous big data analysis
knowledge graph and deep learning in the digital restoration of cultural heritage are also reviewed. To satisfied the interactive 3D display needs of cultural heritage
the content-based natural human-computer interaction technology is introduced
which is combined with text
audio
video
semantics and story retrieval of cultural history. Virtual interactive presentation of new technologies is introduced in terms of real-time realistic technology
virtual-real fusion rendering
and augmented reality technology. Last but not least
in the aspect of intelligent platform construction
there still remains many problems at present
such as the decentralized system management of the cultural heritage intelligent platform
which leads to poor business collaboration
and due to the lack of unified data
it is still difficult to share data mutually. Also
the problems of the difficulty in system maintenance
the high management costs
and the inconvenience for users still need to be solved. In the view of the above mentioned problems
the key technology research of the cultural heritage big data model and the private cloud architecture and the related intelligent platform construction projects are introduced. The above key activation technologies of cultural heritage are actively integrated into the major national development strategies
and these technologies are conducive to excavating and improving the protection and inheritance technology of national cultural heritage
promoting their rational use
and expanding their dissemination influence
which is of great significance to carry forward Chinese civilization
promote cultural prosperity
build demonstration areas for ethnic minorities and enhance cultural confidence.
文化遗产数字化虚拟修复虚拟交互智慧平台
cultural heritagedigitizingvirtual restorationvirtual interactionintelligent platform
Alletto S, Cucchiara R, Del Fiore G, Mainetti L, Mighali V, Patrono L and Serra G. 2016. An indoor location-aware system for an IoT-based smart museum. IEEE Internet of Things Journal, 3(2): 244-253 [DOI: 10.1109/JIOT.2015.2506258]
Arpa S, Süsstrunk S and Hersch R D. 2015. High reliefs from 3D scenes. Computer Graphics Forum, 34(2): 253-263 [DOI: 10.1111/cgf.12557]
Belhumeur P N, Kriegman D J and Yuille A L. 1999. The bas-relief ambiguity. International Journal of Computer Vision, 35(1): 33-44 [DOI: 10.1023/A:1008154927611]
Charalambous E, Dikomitou-Eliadou M, Milis G M, Mitsis G and Eliades D G. 2016. An experimental design for the classification of archaeological ceramic data from Cyprus, and the tracing of inter-class relationships. Journal of Archaeological Science: Reports, 7: 465-471 [DOI: 10.1016/j.jasrep.2015.08.010]
Cignoni P, Montani C and Scopigno R. 1997. Computer-assisted generation of bas-and high-reliefs. Journal of Graphics Tools, 2(3): 15-28 [doi: 10.1080/10867651.1997.10487476]
Cohen F, Liu Z X and Ezgi T. 2013. Virtual reconstruction of archeological vessels using expert priors and intrinsic differential geometry information. Computers and Graphics, 37(1/2): 41-53 [DOI: 10.1016/j.cag.2012.11.001]
Cuomo S, De Michele P, Piccialli F, Galletti A and Jung J E. 2017. IoT-based collaborative reputation system for associating visitors and artworks in a cultural scenario. Expert Systems with Applications, 79: 101-111 [DOI: 10.1016/j.eswa.2017.02.034]
Desai P, Pujari J, Ayachit N H and Prasad V K. 2013. Classification of archaeological monuments for different art forms with an application to CBIR//Proceedings of 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Mysore, India: IEEE [DOI: 10.1109/ICACCI.2013.6637332http://dx.doi.org/10.1109/ICACCI.2013.6637332]
Dinesh C, Cheung G and Bajic I V. 2018.3D point cloud denoising via bipartite graph approximation and reweighted graph laplacian [EB/OL]. [2022-03-06].https://arxiv.org/pdf/1812.07711/pdfhttps://arxiv.org/pdf/1812.07711/pdf
Dinesh C, Cheung G and Bajić V I. 2020. Point cloud denoising via feature graph laplacian regularization. IEEE Transactions on Image Processing, 29: 4143-4158 [DOI: 10.1109/TIP.2020.2969052]
Duan C J, Chen S H and Kovacevic J. 2018. Weighted multi-projection: 3D point cloud denoising with tangent planes//Proceedings of 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Anaheim, USA: IEEE: #8646331 [DOI: 10.1109/GlobalSIP.2018.8646331http://dx.doi.org/10.1109/GlobalSIP.2018.8646331]
Fan Y L, Wang M L and He D J. 2018. Method of point cloud segmentation basded on OpenCL. Computer Engineering and Applications, 54(1): 191-195, 203
范昱伶, 王美丽, 何东健. 2018. 基于OpenCL的点云分割方法. 计算机工程与应用, 54(1): 191-195, 203 [DOI: 10.3778/j.issn.1002-8331.1607-0229]
Geng G H, Feng L, Li K, Zhou M Q and Wang X F. 2021a. A literature review on the digitization and virtual restoration of cultural relics in the Mausoleum of Emperor Qinshihuang. Journal of Northwest University (Natural Science Edition), 51(5): 710-721
耿国华, 冯龙, 李康, 周明全, 王小凤. 2021a. 秦陵文物数字化及虚拟复原研究综述. 西北大学学报(自然科学版), 51(5): 710-721 [DOI: 10.16152/j.cnki.xdxbzr.2021-05-001]
Geng G H, Yao W M, Zhou M Q, Liu J, Xu X L, Cao X, Liu Y Y and Li K. 2021b. Automatic reassembly for cultural relics fragments via adjusting the weight of contour curve. Journal of Northwest University (Natural Science Edition), 51(3): 397-403
耿国华, 姚文敏, 周明全, 刘杰, 徐雪丽, 曹欣, 刘阳洋, 李康. 2021b. 基于调整轮廓线权重的文物碎块自动拼接方法. 西北大学学报(自然科学版), 51(3): 397-403 [DOI: 10.16152/j.cnki.xdxbzr.2021-03-008]
Geng G H, Zhang P F, Liu Y M, Zhou M Q, Yao W M and Li K. 2021c. Reassembly method of cultural relic fragments based on the neighborhood characteristics of fracture surface. Optics and Precision Engineering, 29(5): 1169-1179
耿国华, 张鹏飞, 刘雨萌, 周明全, 姚文敏, 李康. 2021c. 基于断裂面邻域特征的文物碎片拼接. 光学精密工程, 29(5): 1169-1179 [DOI: 10.37188/OPE.20212905.1169]
Guennebaud G and Gross M. 2007. Algebraic point set surfaces. ACM Transactions on Graphics, 26(3): 23.1-23.9 [DOI: 10.1145/1276377.1276406]
Guyot A, Lennon M, Lorho T and Hubert-Moy L. 2021. Combined detection and segmentation of archeological structures from LiDAR data using a deep learning approach. Journal of Computer Applications in Archaeology, 4(1): 1 [DOI: 10.5334/jcaa.64]
Hristov V and Agre G. 2013. A software system for classification of archaeological artefacts represented by 2D plans. Cybernetics and Information Technologies, 13(2): 82-96 [DOI: 10.2478/cait-2013-0017]
Hu J B, Liu Z, Zhang P F, Geng G H and Zhang Y H. 2019. Feature extraction of scattered point clouds based on discrete morse theory. Acta Optica Sinica, 39(6): #0615002
胡佳贝, 刘喆, 张鹏飞, 耿国华, 张雨禾. 2019. 基于离散Morse理论的散乱点云特征提取. 光学学报, 39(6): #0615002 [DOI: 10.3788/AOS201939.0615002]
Hua Z, Lu D M and Pan Y H. 2002. Research on virtual color restoration and gradual changing simulation of dunhuang frasco. Journal of Image and Graphics, 7(2): 181-186
华忠, 鲁东明, 潘云鹤. 2002. 敦煌壁画虚拟复原及演变模拟模型研究. 中国图象图形学报, 7(2): 181-186 [DOI: 10.3969/j.issn.1006-8961.2002.02.017]
Huang Y L and Tan G X. 2012. Research on digital protection and development of China's intangible cultural heritage. Journal of Huazhong Normal University (Humanities and Social Sciences), 51(2): 49-55
黄永林, 谈国新. 2012. 中国非物质文化遗产数字化保护与开发研究. 华中师范大学学报(人文社会科学版), 51(2):49-55
Karasik A and Smilansky U. 2011. Computerized morphological classification of ceramics. Journal of Archaeological Science, 38(10): 2644-2657 [DOI: 10.1016/j.jas.2011.05.023]
Kashihara K. 2012. Three-dimensional reconstruction of artifacts based on a hybrid genetic algorithm//Proceedings of 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Seoul, Korea(South): IEEE: 900-905 [DOI: 10.1109/ICSMC.2012.6377842http://dx.doi.org/10.1109/ICSMC.2012.6377842]
Kerber J, Belyaev A and Seidel H P. 2007. Feature preserving depth compression of range images//Proceedings of the 23rd Spring Conference on Computer Graphics. Budmerice, Slovakia: ACM: 101-105 [DOI: 10.1145/2614348.2614363http://dx.doi.org/10.1145/2614348.2614363]
Kerber J, Tevs A, Belyaev A, Zayer R and Seidel H P. 2009. Feature sensitive bas relief generation//Proceedings of 2009 IEEE International Conference on Shape Modeling and Applications. Beijing, China: IEEE: 148-154 [DOI: 10.1109/SMI.2009.5170176http://dx.doi.org/10.1109/SMI.2009.5170176]
Kersten T P, Pardo C A and Lindstaedt M. 2004.3D acquisition, modelling and visualization of north German castles by digital architectural photogrammetry//Proceedings of the XXth ISPRS Congress, 34(5): 126-131
Kolomenkin M, Leifman G, Shimshoni I and Tal A. 2011. Reconstruction of relief objects from line drawings//Proceedings of the CVPR 2011. Colorado Springs, USA: IEEE: 993-1000 [DOI: 10.1109/CVPR.2011.5995643http://dx.doi.org/10.1109/CVPR.2011.5995643]
Kou J J, Chen X X, Yu Y H, Hai L Q, Zhou P B, Zhang H B and Geng G H. 2021. Compression restoration framework for dense point cloud model of cultural relics. Laser and Optoelectronics Progress, 58(22): #2215006
寇姣姣, 陈小雪, 鱼跃华, 海琳琦, 周蓬勃, 张海波, 耿国华. 2021. 面向文物稠密点云模型的压缩复原框架. 激光与光电子学进展, 58(22): #2215006 [DOI: 10.3788/LOP202158.2215006]
Leonov A V, Anikushkin M N, Ivanov A V, Ovcharov S V, Bobkov A E and Baturin Y M. 2015. Laser scanning and 3D modeling of the Shukhov hyperboloid tower in Moscow. Journal of Cultural Heritage, 16(4): 551-559 [DOI: 10.1016/j.culher.2014.09.014]
Li K, Qian W H, Wang C X and Xu D. 2021. Dongba painting few-shot classification based on graph neural network. Journal of Computer-Aided Design and Computer Graphics, 33(7): 1073-1083
黎克, 钱文华, 王成学, 徐丹. 2021. 基于图神经网络的东巴画小样本分类. 计算机辅助设计与图形学学报, 33(7): 1073-1083 [DOI: 10.3724/SP.J.1089.2021.18618]
Li K, Zhou S P, Zou L B, Deng P and Geng G H. 2017. Sequence images automatic capturing and 3D modeling method for large scale scene based on unmanned aerial vehicle. Journal of Northwest University (Natural Science Edition), 47(1): 30-37
李康, 周泩朴, 邹林波, 邓鹏, 耿国华. 2017. 基于无人机的大场景序列图像自动采集和3维建模. 西北大学学报(自然科学版), 47(1): 30-37 [DOI: 10.16152/j.cnki.xdxbzr.2017-01-006]
Li Z Y, Qian W H, Xu D and Pu Y Y. 2020. Embroidery simulation based on multi-scale two-channel convolution neural network. Journal of Image and Graphics, 25(2): 343-353
李宗彦, 钱文华, 徐丹, 普园媛. 2020. 多尺度双通道卷积神经网络下的刺绣模拟. 中国图象图形学报, 25(2): 343-353 [DOI: 10.11834/jig.190299]
Liao C S, He D J and Wang M L. 2017. Research on a new denoising algorithm for 3D point cloud model. Computer Applications and Software, 34(4): 281-287
廖昌粟, 何东建, 王美丽. 2017. 一种新的3维点云模型去噪光滑算法研究. 计算机应用与软件, 34(4): 281-287 [DOI: 10.3969/j.issn.1000-386x.2017.04.048]
Lipman Y, Cohen-Or D, Levin D and Tal-Ezer H. 2007. Parameterization-free projection for geometry reconstruction. ACM Transactions on Graphics, 26(3): 22.1-22.5 [DOI: 10.1145/1276377.1276405]
Makridis M and Daras P. 2012. Automatic classification of archaeological pottery sherds. Journal on Computing and Cultural Heritage, 5(4): 15 [DOI: 10.1145/2399180.2399183]
Mattei E and Castrodad A. 2017. Point cloud denoising via moving RPCA. Computer Graphics Forum, 36(8): 123-137 [DOI: 10.1111/cgf.13068]
Niu X J, Wang M L and He D J. 2016. A point cloud denoising and smoothing method based on fusion of clustering and filtering. Computer Applications and Software, 33(10): 148-152
牛晓静, 王美丽, 何东健. 2016. 一种聚类与滤波融合的点云去噪平滑方法. 计算机应用与软件, 33(10): 148-152 [DOI: 10.3969/j.issn.1000-386x.2016.10.033]
Öztireli A C, Guennebaud G and Gross M. 2009. Feature preserving point set surfaces based on non-linear kernel regression. Computer Graphics Forum, 28(2): 493-501 [DOI: 10.1111/j.1467-8659.2009.01388.x]
Pan Z G, Chen W Z, Zhang M M, Liu J F and Wu G S. 2009. Virtual reality in the digital Olympic museum. IEEE Computer Graphics and Applications, 29(5): 91-95 [DOI: 10.1109/MCG.2009.103]
Pan Z G, Yuan Q S, Chen S N and Zhang M M. 2020. State of the art on the digital presentation and interaction of culture heritage. Journal of Zhejiang University (Science Edition), 47(3): 261-273
潘志庚, 袁庆曙, 陈胜男, 张明敏. 2020. 文化遗产数字化展示与互动技术研究与进展. 浙江大学学报(理学版), 47(3): 261-273 [DOI: 10.3785/j.issn.1008-9497.2020.03.001]
Papaodysseus C, Arabadjis D, Exarhos M, Rousopoulos P, Zannos S, Panagopoulos M and Papazoglou-Manioudaki L. 2012. Efficient solution to the 3D problem of automatic wall paintings reassembly. Computers and Mathematics with Applications, 64(8): 2712-2734 [DOI: 10.1016/j.camwa.2012.08.003]
Philipp-Foliguet S, Jordan M, Najman L and Cousty J. 2011. Artwork 3D model database indexing and classification. Pattern Recognition, 44(3): 588-597 [DOI: 10.1016/j.patcog.2010.09.016]
Pu Y Y, Su Y, Wei X M, Wei H, Qian W H and Xu D. 2011. System of line drawing for Yunnan heavy color painting. Computer Engineering and Design, 32(2): 607-610, 614
普园媛, 苏迤, 魏小敏, 尉洪, 钱文华, 徐丹. 2011. 云南重彩画白描图绘制系统. 计算机工程与设计, 32(2): 607-610, 614 [DOI: 10.16208/j.issn1000-7024.2011.02.043]
Qian W H, Xu D, Cao J D, Guan Z and Pu Y Y. 2019. Aesthetic art simulation for embroidery style. Multimedia Tools and Applications, 78(1): 995-1016 [DOI: 10.1007/s11042-018-6002-9]
Qian W H, Xu D, Xu J, He L and Han Z Y. 2019. Artistic paintings classification based on information entropy. Journal of Graphics, 40(6): 991-999
钱文华, 徐丹, 徐瑾, 何磊, 韩镇阳. 2019. 基于信息熵的风格绘画分类研究. 图学学报, 40(6): 991-999 [DOI: 10.11996/JG.j.2095-302X.2019060991]
Qian W H, Xu D, Xu J, He L and Zhang B. 2020. Simulation of dongba art style painting. Journal of System Simulation, 32(7): 1349-1359
钱文华, 徐丹, 徐瑾, 何磊, 张波. 2020. 东巴画艺术风格绘制. 系统仿真学报, 32(7): 1349-1359 [DOI: 10.16182/j.issn1004731x.joss.19-VR0530]
Rasheed N A and Nordin J. 2015. Archaeological fragments classification based on rgb color and texture features. Journal of Theoretical and Applied Information Technology, 76(3): 358-365
Sarkar K, Bernard F, Varanasi K, Theobalt C and Stricker D. 2018. Structured low-rank matrix factorization for point-cloud denoising//Proceedings of 2018 International Conference on 3D Vision (3DV). Verona, Italy: IEEE [DOI: 10.1109/3DV.2018.00058http://dx.doi.org/10.1109/3DV.2018.00058]
Schoenenberger Y, Paratte J and Vandergheynst P. 2015. Graph-based denoising for time-varying point clouds//Proceedings of 2015 3DTV-Conference: the True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON). Lisbon, Portugal: IEEE [DOI: 10.1109/3DTV.2015.7169366http://dx.doi.org/10.1109/3DTV.2015.7169366]
Shang J, Yu W J and Wang M L. 2020. Bas-relief generation controlled by different height maps. Journal of Image and Graphics, 25(12): 2647-2655
尚菁, 余文杰, 王美丽. 2020. 不同类型高度图控制浅浮雕模型生成方法. 中国图象图形学报, 25(12): 2647-2655 [DOI: 10.11834/jig.190630]
Smith P, Bespalov D, Shokoufandeh A and Jeppson P. 2010. Classification of archaeological ceramic fragments using texture and color descriptors//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. San Francisco, USA: IEEE: 49-54 [DOI: 10.1109/CVPRW.2010.5543523http://dx.doi.org/10.1109/CVPRW.2010.5543523]
Sohn B S. 2016. Ubiquitous creation of digital bas-reliefs using smartphone//Proceedings of 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN). Vienna, Austria: IEEE [DOI: 10.1109/ICUFN.2016.7537138http://dx.doi.org/10.1109/ICUFN.2016.7537138]
Son K, Almeida E B and Cooper D B. 2013. Axially symmetric 3D pots configuration system using axis of symmetry and break curve//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE: #40 [DOI: 10.1109/CVPR.2013.40http://dx.doi.org/10.1109/CVPR.2013.40]
Wang M L, Yang L Y, Geng N and He D J. 2018. Survey and prospect of 3D model-based digital relief generation. Journal of Image and Graphics, 23(9): 1273-1284
王美丽, 杨丽莹, 耿楠, 何东健. 2018. 基于3维模型的数字浮雕生成技术. 中国图象图形学报, 23(9): 1273-1284 [DOI: 10.11834/jig.170642]
Wang S R, Guo B, Guo R Z, Geng G H, Zhou X Y and Zhang Y H. 2021. Fuzzy sets based feature extraction from point clouds of pottery cultural relics fragments. Journal of Northwest University (Natural Science Edition), 51(5): 750-758
王淑睿, 郭宝, 郭锐哲, 耿国华, 周芯羽, 张雨禾. 2021. 基于模糊集的陶质文物碎片点云特征提取算法. 西北大学学报(自然科学版), 51(5): 750-758 [DOI: 10.16152/j.cnki.xdxbzr.2021-05-005]
Wang Y J, Geng G H, Guo P Y, Tuo D C, Jing Y P, Zhu X Y and Liu X N. 2021. Realistic processing of restored face based on adversarial generative network. Journal of Northwest University (Natural Science Edition), 51(5): 742-749
王跃进, 耿国华, 郭沛瑶, 拓东成, 景云鹏, 朱新懿, 刘晓宁. 2021. 基于对抗生成网络的复原面貌真实感处理. 西北大学学报(自然科学版), 51(5): 742-749 [DOI: 10.16152/j.cnki.xdxbzr.2021-05-004]
Wei X D, Weng D D, Liu Y and Wang Y T. 2016. A tour guiding system of historical relics based on augmented reality//Proceedings of 2016 IEEE Virtual Reality (VR). Greenville, USA: IEEE: 307-308 [DOI: 10.1109/VR.2016.7504776http://dx.doi.org/10.1109/VR.2016.7504776]
Wen L L, Xu D, Zhang X and Qian W H. 2019. The inpainting of irregular damaged areas in ancient murals using generative model. Journal of Graphics, 40(5): 925-931
温利龙, 徐丹, 张熹, 钱文华. 2019. 基于生成模型的古壁画非规则破损部分修复方法. 图学学报, 40(5): 925-931 [DOI: 10.11996/JG.j.2095-302X.2019050925]
Weyrich T, Deng J, Barnes C, Rusinkiewicz S and Finkelstein A. 2007. Digital bas-relief from 3D scenes. ACM Transactions on Graphics, 26(3): #32 [DOI: 10.1145/1276377.1276417]
Windhager F, Federico P, Schreder G, Glinka K, Dörk M, Miksch S and Mayr E. 2019. Visualization of cultural heritage collection data: State of the art and future challenges. IEEE Transactions on Visualization and Computer Graphics, 25(6): 2311-2330 [DOI: 10.1109/TVCG.2018.2830759]
Wu Y H and Zhou M Q. 2009. Application of 3D scanning technique in heritage protection. Computer Technology and Development, 19(9): 173-176
吴玉涵, 周明全. 2009.3维扫描技术在文物保护中的应用. 计算机技术与发展, 19(9): 173-176 [DOI: 10.3969/j.issn.1673-629X.2009.09.048]
Xu J, Pu Y Y, Xu D, Zhao Z P, Qian W H, Wu H and Yang Q X. 2021. Research on virtual try-on via style transformation and local rendering. Journal of Taiyuan University of Technology, 52(1): 98-104
徐俊, 普园媛, 徐丹, 赵征鹏, 钱文华, 吴昊, 阳秋霞. 2021. 基于款式变换和局部渲染相结合的虚拟试衣研究. 太原理工大学学报, 52(1): 98-104 [DOI: 10.16355/j.cnki.issn1007-9432tyut.2021.01.013]
Yang W, Zhou M Q, Geng G H, Liu X N, Li K and Zhang H B. 2019. Hierarchical optimization of skull point cloud registration. Optics and Precision Engineering, 27(12): 2730-2739
杨稳, 周明全, 耿国华, 刘晓宁, 李康, 张海波. 2019. 层次优化的颅骨点云配准. 光学精密工程, 27(12): 2730-2739 [DOI: 10.3788/OPE.20192712.2730]
Yu Y H, Zhang H B, Li X, Kou J J, Li K, Geng G H and Zhou M Q. 2021. Data enhanced depth classification model for terra-cotta warriors fragments. Laser and Optoelectronics Progress, 1-20
鱼跃华, 张海波, 李昕, 寇姣姣, 李康, 耿国华, 周明全. 2021. 基于数据增强的秦俑碎片深度分类模型. 激光与光电子学进展, 1-20
Yu Y T, Xu D and Qian W H. 2019. Simulation of batik cracks and cloth dying. Scientia Sinica (Informationis), 49(2): 159-171
喻扬涛, 徐丹, 钱文华. 2019. 蜡染冰纹生成与染色模拟. 中国科学: 信息科学, 49(2): 159-171
Yu Y T, Yu Z L, Qian W H, Zhang K S and Xu D. 2018. Batik dying simulation based on diffusion. Journal of System Simulation, 30(6): 2117-2124
喻扬涛, 俞振璐, 钱文华, 张可师, 徐丹. 2018. 基于扩散的蜡染染色模拟. 系统仿真学报, 30(6): 2117-2124 [DOI: 10.16182/j.issn1004731x.joss.201806015]
Zhang J, Zhou M Q, Zhang Y H and Geng G H. 2016. Global feature extraction from scattered point clouds based on markov random field. Acta Automatica Sinica, 42(7): 1090-1099
张靖, 周明全, 张雨禾, 耿国华. 2016. 基于马尔科夫随机场的散乱点云全局特征提取. 自动化学报, 42(7): 1090-1099 [DOI: 10.16383/j.aas.2016.c150627]
Zhang J, Zhou M Q, Zhang Y H, Geng G H and Li S S. 2017. Study on a new method of feature extraction from scattered point clouds. Journal of Chinese Computer Systems, 38(7): 1601-1607
张靖, 周明全, 张雨禾, 耿国华, 李姗姗. 2017. 一种新的散乱点云特征提取方法研究. 小型微型计算机系统, 38(7): 1601-1607
Zhang M M, Chen X and Pan Z G. 2014. Interaction study of digital shadow play using the Kinect//Proceedings of the SIGGRAPH Asia 2014 Posters. Shenzhen, China: ACM: #17 [DOI: 10.1145/2668975.2669006http://dx.doi.org/10.1145/2668975.2669006]
Zhao F Q, Dai C and Geng G H. 2021.3D model retrieval of terracotta warriors fragments based on feature fusion. Journal of University of Electronic Science and Technology of China, 50(2): 225-230
赵夫群, 戴翀, 耿国华. 2021. 基于特征融合的文物碎片模型检索. 电子科技大学学报, 50(2): 225-230 [DOI: 10.12178/1001-0548.2020281]
Zheng R, Qian W H, Xu D and Pu Y Y. 2019. Synthesis of embroidery based on convolutional neural network. Journal of Zhejiang University (Science Edition), 46(3): 270-278
郑锐, 钱文华, 徐丹, 普园媛. 2019. 基于卷积神经网络的刺绣风格数字合成. 浙江大学学报(理学版), 46(3): 270-278 [DOI: 10.3785/j.issn.1008-9497.2019.03.002]
Zhou M Q, Yang W, Lin F Y, Geng G H, Liu X N and Li K. 2021. Skull identification based on least square canonical correlation analysis. Optics and Precision Engineering, 29(1): #201
周明全, 杨稳, 林芃樾, 耿国华, 刘晓宁, 李康. 2021. 基于最小二乘正则相关性分析的颅骨身份识别. 光学精密工程, 29(1): 201-210 [DOI: 10.37188/OPE.20212901.0201]
Zhou S P, Geng G H, Li K and Wang P. 2018. Multi-view geometric 3D reconstruction method based on AKAZE algorithm. Computer Science, 45(11A): 180-184, 207
周泩朴,耿国华, 李康, 王飘. 2018. 一种基于AKAZE算法的多视图几何3维重建方法. 计算机科学, 45(11A): 180-184, 207
相关作者
相关机构