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1 TVRSJ Vol.10 No.3, Digital Restoration of The Original Great Buddha and Main Hall of Todaiji Temple Takeshi Oishi 1, Tomohito Masuda 2, Ryo Kurazume 3 and Katsushi Ikeuchi 4 Abstract We digitally restored the original Great Buddha and the main hall of Todaiji temple. Todaiji temple has been destroyed by natural and artificial disasters and rebuilt for a number of times. As a result, the shapes of the current Great Buddha and its main hall are slightly different from the original states. We reconstructed the 3D models of the original Great Buddha and its main hall. The 3D model of the original Great Buddha was reconstructed by morphing the 3D model of the current Great Buddha, which was obtained by laser scanning and Modeling-from-Reality techniques. The 3D model of the original main hall was reconstructed by assembling the partial 3D models of the main hall of Toshodaiji temple. We combined these models and visualized the original state of Todaiji temple by Computer Graphics techniques. Keywords : 3D shape modeling, cultural heritage, digital archive, digital restoration CG [1], [2] 3 CAD *1 *2 *3 *4 *1 Institute of Industrial Science, The University of Tokyo *2 Graduate School of Information Science and Technology, The University of Tokyo *3 Graduate School of Information Science and Electorical Engineering, Kyushu University *4 Interfaculty Initiative in Information Studies, The University of Tokyo 1 Fig. 1 Nara Great Buddha VR AR CG 3 3 CAD CAD CAD 3 Modeling From Reality [3], [4] 3
2 Vol.10, No.3, Fig. 2 Range images of Nara Great Buddha CAD CG 6 2. (1) (2) (3) m 2m 0.1mm 50m 100m mm Time-of-Flight 3 Fig. 3 Alignment result Cyrax2400[5] ,299,982 69,162, Besl Iterative Closest Point ICP [6] Chen [7] 2 [8], [9] 3
![: 5 3 Fig. 5 3D model of current Great Buddha 4 Fig. 4 Merging result 1 [m] [m] 14.98 15.85 1.02 1.16 3.20 2.82 2.54 2.52 1.48 1.66 3.74 3.56 2.23 2.07 5.33 4.](/docs-images/91/107165751/images/3-0.jpg "74 0.50 0.47 1.33 1.10 2. 3 Zipper [10] [11], [12] Wheeler Wheeler PC [13] 4 3 6 3 Fig. 6 3D model of original Great Buddha [14] 1,560,000 3,000,000 3. 3 3 CAD 3.")
3 : 5 3 Fig. 5 3D model of current Great Buddha 4 Fig. 4 Merging result 1 [m] [m] Zipper [10] [11], [12] Wheeler Wheeler PC [13] Fig. 6 3D model of original Great Buddha [14] 1,560,000 3,000, CAD 3. 1
![Vol.10, No.3, 2005 [15] 3 1 1 0.2964 3. 2 1 2 2 [15] 3 6 5 4. 3 2 12 18 12 18 12 7 Fig.](/docs-images/91/107165751/images/4-0.jpg "7 Presumed miniature 1. 2. 3 1. 3 2. 7 3 4. 1 3 TDS-130L TDS-130L 3.5m 10m 0.")
4 Vol.10, No.3, 2005 [15] [15] Fig. 7 Presumed miniature TDS-130L TDS-130L 3.5m 10m 0.5mm 5mm 400x
5 大石 増田 倉爪 池内 : 創建期奈良大仏及び大仏殿のデジタル復元 図 8 推定模型の 3 次元モデル Fig. 8 3D model of presumed miniature 図 10 唐招提寺金堂の部分モデル Fig. 10 Partial 3D models of Toshodaiji Temple 図 9 唐招提寺金堂の測定部位 Fig. 9 Measured Parts of Toshodaiji Temple 図 11 創建期大仏殿の 3 次元モデル Fig. 11 3D model of original main hall of Todaiji Temple こで 得られたモデルを 50 倍に拡大して使用した 4. 2 唐招提寺金堂の部分モデル取得 次に 細部の復元に必要となる唐招提寺金堂の部位 あった 模型モデルと部位モデルの寸法を比較すると ほぼ全ての部位は 2.3 倍にすれば良いことが分かった モデルを取得した まず 建物全体をいくつかの部位 正確には 組物の肘木の大きさは軒の深さによって寸 に分けて 主要な部位を選択して 20 箇所の測定を行っ 法を変える必要があり 斗の大きさも柱の太さや屋根 た 図 9 に測定対象とした主要部位の一部を示す 測 の重さによって異なってくる しかし 本研究は見え 定に用いたレンジセンサは Cyrax2400 及びパルステッ の生成が主な目的であるため 各部位を単純拡大する ク社製の TDS-1500 と TDS-3100 である TDS-1500 だけに留めた ただし 扉などは高さと幅の縮尺が異 の性能はスキャン深度 0.5m 1m で分解能 0.23mm なるため 必要に応じて調整を行った また 壁や一部 0.83mm である また TDS-3100 はスキャン深度 1.0m 3m で分解能は 0.83mm 6.7mm である 屋根や柱 の柱などの単純な部位は 直方体や円筒のモデルを組 扉などの大きな部位は Cyrax2400 を用いて測定を行っ 本口三尺八寸 末口三尺という値に合わせた [17] 図 た また 組物や軒先 風鐸など近距離でしか測定で 11 に 完成した創建期大仏殿の 3 次元モデルを示す きないものは TDS-1500 と TDS-3100 を用いた この 測定によって計 780 枚の距離画像を取得した 図 10 に これらの距離画像から得られた 3 次元モデルの一 部を示す 4. 3 創建期大仏殿の復元 最後に 得られた部位モデルを組合わせる事によっ み合わせた このとき 柱の太さは七大寺巡禮私記の 5. 創建期東大寺の再現及び鍍金に関する考察 本章では 復元したモデルを組合せて 創建期奈良 大仏及び大仏殿の様子を CG によって再現する また 復元モデルの表面積から 大仏に施された鍍金につい て考察する 創建期奈良大仏及び大仏殿の再現 て創建期大仏殿モデルの復元を行った 各部位モデル 5. 1 は 予め編集しやすいようにメッシュ数を減少させて 復元した奈良大仏及び大仏殿を用いて CG による ある [16] また 唐招提寺金堂と東大寺大仏殿は規模が 創建当時の様子の再現を行った 柱や扉などの色は丹 異なるため 取得した各部位モデルを拡大する必要が 土色とした また鴟尾と大仏本尊の表面には鍍金が施
![Vol.10, No.3, 2005 2 [kg] [µm] 227.0 19.69 75.67 6.563 175.](/docs-images/91/107165751/images/6-0.jpg "6 15.23 58.54 5.077 12 CG Fig.")
6 Vol.10, No.3, [kg] [µm] CG Fig. 12 Original main hall of Todaiji Temple(CG) 13 CG Fig. 13 Original Nara Great Buddha in the original main hall of Todaiji Temple(CG) Alias r MAYA r Blinn [18] 506m 2 597m 2 556m =41.94g 1 = 13.98g 19300kg/m 2 2 1µm 10µm [18] 6.0µm [18] 5.0µm
7 : CG 58.54kg 5µm CREST [1],,,,,, : VR - -,, Vol. 8, No. 1, pp.65-74, [2] R. Tenmoku, Y. Nakazato, A. Anabuki, M. Kanbara, and N. Yokoya : Nara palace site navigator: Device-independent human navigation using a networked shared database, Proc.10th Int. Conf. on Virtual Systems and Multimedia (VSMM2004), pp , Nov [3] M. Levoy : The Digital Michelangelo Project, Proc. SIGGRAPH 2000, pp , [4] K. Ikeuchi : Modeling from Reality, Proc. Third International Conference on 3D Digital Imaging and Modeling (3DIM 01), [5] [6] P. Besl and N. McKay : A Method for Registration of 3-D Shapes, IEEE Trans. PAMI, Vol. 14, No. 2, pp , February [7] Y. Chen and G. Medioni : Object modelling by registration of multiple range images, Image and Vision Computing, Vol. 10, No. 3, pp , [8] P.J. Neugebauer : Reconstruction of Real-World Objects via Simultaneous Registration and Robust Combination of Multiple Range Images. International Journal of Shape Modeling, 3(1&2):71-90, Mar [9] K. Nishino and K. Ikeuchi : Robust simultaneous registration of multiple range images. In Proc. of Fifth Asian Conference on Computer Vision ACCV 02, pages , [10] G. Turk and M. Levoy : Zippered polygon meshes from range images. In Proceedings of SIG- GRAPH 94, pages , July [11] B. Curless and M. Levoy : A Volumetric Method for Building Complex Models from Range Images, Proc. SIGGRAPH 96, ACM, pp , [12] M. Wheeler, Y. Sato and K. Ikeuchi : Consensus surfaces for modeling 3D objects from multiple range images, Proc. 6th Int. Conf. Comp. Vision, pp , [13] R. Sagawa and K. Nishino and M.D. Wheeler and K. Ikeuchi : Parallel Processing of Range Data Merging Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, vol.1, pp , [14] W. Lorensen and H. Cline : Marching cubes: a high resolution 3d surface construction algorithm. In Proc. SIGGRAPH 87, pp , ACM, [15] :,,, [16] M. Garland and P. Heckbert : Surface Simplification Using Quadric Error Metrics, In Proc. SIG- GRAPH 97, ACM, [17] :, 15, ( ), pp.14-34,,, [18] :,, Vol. 24, No.2, (No. 16),
8 Vol.10, No.3, MIT CMU D.Marr ICCV:1990 IEEE CVPR:1991 AI Journal:1992 Fu IEEE Trans. R&A 1998 IEEE Distinguished Lecturer SPS CS IEEE Fellow
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The Great Buddha Project Λ1 Λ2 Λ2 Λ2 Λ3 Λ2 Λ1 Λ2 Λ3 ( (VR) VR (1) (2) (3) modeling-from-reality (MFR) The Great Buddha Project Digital archive of large-scale cultural heritage Ryo Kurazume Λ1 Ko Nishino
miru02_merging.dvi
1 PC 2 Geometric and Photometric Integration System for Large Objects Ryusuke Sagawa Ko Nishino Ryo Kurazume Katsushi Ikeuchi The University of Tokyo Kyushu University Some researchers have begun projects
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M. D. Wheler Cyra Technologies, Inc. 3 3 CAD albedo Mapping textures on 3D geometric model using reflectance image Ryo Kurazume M. D. Wheler Katsushi Ikeuchi The University oftokyo Cyra Technologies, Inc.
258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
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1234 Vol. 25 No. 8, pp , 2007 CPS SLAM Study on CPS SLAM 3D Laser Measurement System for Large Scale Architectures Ryo Kurazume,Yukihiro Toba
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4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
![4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q](/thumbs/92/110416683.jpg "4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q")
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
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ICS-06M-404 255 1 7 1.1................................... 7 1.1.1........................... 7 1.1.2........................ 8 1.1.3............................ 9 1.2..................................
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14 2 5 i ii Surface Reconstruction from Point Cloud of Human Body in Arbitrary Postures Isao MORO Abstract We propose a method for surface reconstruction from point cloud of human body in arbitrary postures.
A Survey of Shape Model Generation from Multiple Range Images (1) ( ) (2) (3) Takeshi Masuda(1), Ikuko Shimizu Okatani(2), Ryusuke Sagawa(3) 1: Nation
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(MIRU2005) ( ) Radim Šára Globally Optimal Range Image Registration by G
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(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
![(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s](/thumbs/75/72066128.jpg "(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s")
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3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)
![3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)](/thumbs/68/59204737.jpg "3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)")
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Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
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![(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc](/thumbs/65/54056306.jpg "(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc")
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36 Kitchen of the Future: Kitchen of the Future Kitchen of the Future A kitchen is a place of food production, education, and communication. As it is more active place than other parts of a house, there
1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +
![1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +](/thumbs/81/83701592.jpg "1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +")
3 3D 1,a) 1 1 Kinect (X, Y) 3D 3D 1. 2010 Microsoft Kinect for Windows SDK( (Kinect) SDK ) 3D [1], [2] [3] [4] [5] [10] 30fps [10] 3 Kinect 3 Kinect Kinect for Windows SDK 3 Microsoft 3 Kinect for Windows
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[2][3][4][5] 4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) 2. Shiratori [2] Shiratori [3] [4] GP [5] [6] [7] [8][9] Kinect Choi [10] 3. 1 c 2016 Information Processing So
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[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing
![[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing](/thumbs/93/112070099.jpg "[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing")
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DPA,, ShareLog 3) 4) 2.2 Strino Strino STRain-based user Interface with tacticle of elastic Natural ObjectsStrino 1 Strino ) PC Log-Log (2007 6)
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