九州大学学術情報リポジトリ Kyushu University Institutional Repository 多視点動画像処理による 3 次元モデル復元に基づく自由視点画像生成のオンライン化 : PC クラスタを用いた実現法 上田, 恵九州大学システム情報科学研究院知能システム学部門 有田, 大
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1 九州大学学術情報リポジトリ Kyushu University Institutional Repository 多視点動画像処理による 3 次元モデル復元に基づく自由視点画像生成のオンライン化 : PC クラスタを用いた実現法 上田, 恵九州大学システム情報科学研究院知能システム学部門 有田, 大作九州大学システム情報科学研究院知能システム学部門 谷口, 倫一郎九州大学システム情報科学研究院知能システム学部門 出版情報 : 情報処理学会論文誌. 46 (11), pp , 情報処理学会バージョン : 権利関係 : ここに掲載した著作物の利用に関する注意本著作物の著作権は ( 社 ) 情報処理学会に帰属します 本著作物は著作権者である情報処理学会の許可のもとに掲載するものです ご利用に当たっては 著作権法 ならびに 情報処理学会倫理綱領 に従うことをお願いいたします
2 Vol. 46 No. 11 Nov PC Z On-line free-viewpoint video generation based on a 3D model reconstructed from multi-viewpoint videos -Implementation on a PC-cluster- Megumu Ueda, Daisaku Arita and Rin-ichiro Taniguchi Recently, there are a lot of researches for generating free-viewpoint videos by reconstructing 3D models from multiple camera images. Since it is difficult to generate free-viewpoint videos on-line for the large amount of computation, most of these researches aim to generate freeviewpoint videos off-line, or generate free-viewpoint videos without 3D model reconstruction. In this paper, we will propose a method that generates free-viewpoint videos by reconstructing 3D models on-line. The method first reconstructs 3D models by visual cone intersection method using multiple cameras, second colors the surfaces of 3D models in terms of triangular patch representation, and displays the colored models on a screen on-line. In these procedures, it is difficuld to color the surfaces. Then, we propose a new method for coloring, which is based on the Z-buffer method. And we show generated free-viewpoint images to estimate the method. 1. Virtualized Reality 1) Department of Intelligent Systems, Kyushu University Department of Intelligent Systems, Kyushu University 3 3 Light Field 2) 3 3) 1234
3 Vol. 46 No )5)6) Light Field 3 Matusik 7) 3 3 8)9)10) M. Gross 11) O. Grau 12) 13) PC 3 PC PC ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) PC 2.2 PC
4 1236 Nov Data flow Capture and create visual cone A A B Visual cone intersection C Coloring D D Integration of color infomation and Creating a free-viewpoint image E Voxel space Object Camera (a) Voxel space Object Camera (b) A A A' A' B B 1 Fig. 1 D D System configuration Voxel Camera image Color Camera PC PC RPV Real-time Parallel Vision 14) 3 PC PC AA 2 B C D E AA : 2 Fig. 2 Visual cone intersection: (a) Visual cone construction, (b) Visual cone intersection. A A AA BC: 3 2(a) AA BC 2(b) BC C 15) C D /1
5 Vol. 46 No E D: A C 3.2 E: C D 3.3 OpenGL OpenGL OS RPV 14) 1 1 D C 3. DE D E 3.1 ( 1 ) ( 2 ) 4) ) 3.2 D 3.3 E 3.2 n n O(n 2 ) Z Z Z
6 1238 Nov Z Z 1: 2: Z 4 : Z 1: 1 1 Z 5(a)) Z 2: 2 2 5(b) 1 Z Z 1 Z 2 (a) (b) 3 Fig. 3 Dividing triangular patch: (a) Original triangular patch, (b) Divided triangular patch. 2 O(n) 3.3 N n(1 n N) θ n 6 n W n θ n W n = (cosθ n + 1) α N (cosθ k + 1) α k=0 cosθ cosθ + 1 α
7 Vol. 46 No image plane normal vector viewpoint object surface (a) 1 image plane normal vector viewpoint object surface (b) 2 4 Fig. 4 Flowchart of vertex coloring Fig. 5 5 Vertex coloring: (a) Step1, (b) Step PC A 6 A 1 B 2 C 1 D 6 E 1 PC LAN Myrinet 1Gb/sec 7 IEEE ) 7 A 1 PC Table 1 Performance of PC OS Red Hat Linux9 CPU Intel Pentium4 3GHz 1GB gcc Tsai 17) cm PC 1
8 1240 Nov Object surface Camera 1 Camera 2 Object 1 2 N Virtual viewpoint Camera N 6 Fig. 6 Angle between camera and virtual viewpoint Camera 7 Fig. 7 Camera arrangement 3.3 α α = fps 8(a)() 9() 8(b) (d) 9() (a) 2 11(a) cam7 cam8cam9 cam8 cam9 RGB 2 RGB ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) 3 OpenGL 2 4.4
9 Vol. 46 No. 11 多視点動画像処理による 3 次元モデル復元に基づく自由視点画像生成のオンライン化 (a) 原画像 1 (b) 生成画像 1 (c) 原画像 2 (d) 生成画像 図 8 原画像と生成された自由視点画像 Fig. 8 Camera iamges(left) and generated images(right) 図 9 (上) 原画像と (下) 生成された自由視点画像 Fig. 9 Camera images(upper) and generated images(lower) 測した 図 11(b)(c) に処理時間を示す 平均では 20fps 程 度の速度での処理が可能であった これは実用十分な 速度が得られていると言える しかし図 11(b) から 処理速度は安定していないことがわかる これは対象 物体の大きさや形によって三角パッチの数が変わるか らである 4.5 遅延時間についての考察 遅延時間は 全 PC の内部時計は ntp を利用するこ とにより一致していると仮定して カメラ画像が入力
10 1242 Nov Fig Generated virtual-viewpoint images 2 2 Table 2 Average and variance of r.m.s. errors of coloring Table 3 Amount of data transfer of each node (Kbyte) A (Image) 93.5 (variable) A and B (voxel) (constant) C 28.6 (variable) D 92.0 (variable) 11(d) C BC Myrinet BC ( 11(b)(c)) 20fps 5. PC 20fps :
11 Vol. 46 No Root-mean-square errors Frame number cam1 cam2 cam3 cam4 cam5 cam6 cam7 cam8 cam9 (a) (b) (c) (d) Fig Measurement result: (a) Coloring error, (b) Processing time from each pipeline, (c) Processing time from each node, (d) Latency. : A A A 1) Kanade, T., Rander, P. W. and Narayanan, P. J.: Concepts and early results, IEEE Workshop on the Representation of Visual Scenes, pp (1995). 2) Levoy, M. and Hanrahan, P.: Light field rendering, Proc. of SIGGRAPH, pp (1996). 3) Martin, W. N. and Aggarwal, J. K.: Volumetric Description of Objects from Multiple Views, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 5, No. 2, pp (1983).
12 1244 Nov ) 3 Vol. 56, No. 4, pp (2002). 5) Vol.43, No.SIG 11(CVIM 5), pp (2002). 6) Goldlucke, B. and Magnor, M.: Real-Time Microfacet Billboarding for Free-Viewpoint Video Rendering, Proc. IEEE International Conference on Image Processing (ICIP 03), Vol. 3, pp (2003). 7) Matusik, W., Buehler, C., R.Raskar, Gortler, S. and McMilla, L.: Image-Based Visual Hulls, Proc. of SIGGRAPH, pp (2000). 8) CVIM Vol. 42, No. SIG 6(CVIM 2), pp (2001). 9) 3 7 pp (2001). 10) Cheung, G. K. M., Kanade, T., Bouguet, J. Y. and Holler, M.: A real time system for robust 3D voxel reconstruction of human motions, Proc. CVPR2000, Vol. 2 (2000). 11) Gross, M., Wurmlin, S., Naef, M., Lamboray, E., C.Spagno, A.Kunz, Koller-Meier, E., Gool, T. S. L.V., Lang, S., Strehlke, K., Moere, A. V. and O, S.: blue-c: A Spatially Immersive Display and 3D Video Portal for Telepresence, Proceedings of ACM SIGGRAPH2003, pp (2003). 12) Grau, O., Pullen, T. and Thomas, G. A.: A Combined Studio Production System for 3D Capturing of Live Action and Immersive Actor Feedback, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 3, pp (2004). 13) 3 (MIRU2004)pp (2004). 14) Vol. 143, No. No. SIG 11(CVIM5), pp (2002). 15) pp (1999). 16) pp (2000). 17) Tsai, R. Y.: A Versatile Camera Calibration Technique forhigh-accuracy 3D Machine Vision Metrology UsingOff-the-Shelf TV Cameras and Lenses, IEEE Trans. on Robotics and Automation, Vol. 3, No. 4, pp (1987). ( ) ( )
1 Table 1: Identification by color of voxel Voxel Mode of expression Nothing Other 1 Orange 2 Blue 3 Yellow 4 SSL Humanoid SSL-Vision 3 3 [, 21] 8 325
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![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 +")
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14 2 5
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1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D
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