3 3 3 Knecht (2-3fps) AR [3] 2. 2 Debevec High Dynamic Range( HDR) [4] HDR Derek [5] 2. 3 [6] 3. [6] x E(x) E(x) = 2π π 2 V (x, θ i, ϕ i )L(θ
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1 (MIRU212) RGB-D {ikeda,charmie,saito}@hvrl.ics.keio.ac.jp, [email protected] RGB-D Lambert RGB-D 1. Augmented Reality AR [1] AR AR 2 [2], [3] [4], [5] [6] RGB-D RGB-D RGB-D [2]
2 3 3 3 Knecht (2-3fps) AR [3] 2. 2 Debevec High Dynamic Range( HDR) [4] HDR Derek [5] 2. 3 [6] 3. [6] x E(x) E(x) = 2π π 2 V (x, θ i, ϕ i )L(θ i, ϕ i ) cos θ i sin θ i dθdϕ, (1) L(θ i, ϕ i ) (θ i, ϕ i ) cos θ i sin θ i dθ i dϕ i E(x) V (x, θ i, ϕ i ) x (θ i, ϕ i ) V = V = 1 x x (θ r, ϕ r ) u i(θ r, ϕ r ) (θ i, ϕ i ) (θ r, ϕ r ) (Bidirectional Reflectance Distribution Function: BRDF) i(θ r, ϕ r ) = k 2π π 2 V (θ i, ϕ i )R(θ i, ϕ i, θ r, ϕ r ) L(θ i, ϕ i ) cos θ i sin θ i dθdϕ, (2) k k R(θ i, ϕ i, θ r, ϕ r ) BRDF Lambert BRDF R d i(θ r, ϕ r ) = k 2π π 2 V (x, θ i, ϕ i )R d L(θ i, ϕ i ) cos θ i sin θ i dθdϕ (3) 2 θ i ϕ i N
3 i(u) = = V n (x)r d L n cos θ n S n L n, (4) i(u) u V n (x) n x R d L n k n θ n z n S n = V n (x)r d cos θ n L n S n N < = M L n. i = SL i(u 1 ) S 11 S 1N. =.. S.. mn i(u M ) S M1 S MN L 1.. L N (5) (5) [7], [8] (5) L n V n (x) cos θ n R d R d R d V n (x) u i (u) i (u) = R d L n cos θ n (6) (4) R d i(u) N i (u) = L n N j=1 L V n (x) cos θ n (7) j cos θ j R d (7) L n L n N j=1 L j cos θ j R d V n (x) cos θ n 3 4. RGB-D V n (x) cos θ n 3. 2 x E in (x) L E in (x) = L n cos θ n (8) (1) x E out (x) E out (x) = V n (x)l n cos θ n (9) V n (x) x n x u i in (u) E in (x) u i out (u) E out (x) E in (x) E out (x) i out (u) 4. i out (u) = i in (u) E out(x) E in (x) (1) RGB-D 3 V n (x) cos θ n V n (x) cos θ n 3 RGB RGB 3 3
4 2 V n (x) cos θ n L L RGB 4 (c obj, c bg, d obj, d bg ) 2 c, d RGB obj, bg diff c obj if c obj c bg > τ c c diff = (11) otherwise d obj if d obj d bg > τ d d diff = (12) otherwise τ d diff a obj = d diff (13) RGB RGB RGB RGB c diff d diff 3 3 a shadow = c diff ddiff (14) a obj a shadow d obj V n (x) cos θ n 4. 2 V n (x) cos θ n 3 V n (x) cos θ n V n (x), 3 x n V n (x) = 2 V n (x) = V n (x) 3 V n (x) = 1 3 V n (x) = 1
5 V n (x) = 1 cos θ n 3 x n θ n. 5.,, (4) (5) 4 Box 7 [6] Box Box V n (x) cos θ n Box RGB-D 5 2 RGB-D Microsoft Kinect < = θ < = 7 5 < = ϕ < θ (Box, Duck2, Hemisphere) 7 (Combined) Box ( ) 5 2 RGB-D V n (x) V n (x) = 5. 3 Ground truth 5 3 Box Ground truth 5 Ground truth Real scene 2 Real scene 2 Box 2 Box Duck2 2 Box Duck2 Box RGB-D
6 4. V n (x) Hemisphere Box 2 Combined Duck2 Box Duck2 Duck2 V n (x) 5. 4 Box Duck2 2 1 Box Duck2 V n (x) 3(c) 2 3(a) (b) Hemisphere Box, Combined (Duck2 Hemisphere ) Hemisphere 6. RGB-D RGB-D V n (x) V n (x)., [9] [1] R. Azuma, A survey of augmented reality, Presence: Teleoperators and Virtual Environments, 6 (1997). [2], VRSJ, 4, 4, pp
![5 (1999). [3] M. knecht, et al., Differential instant radiosity for mixed reality, in Proc. ISMAR (21). [4] P.](/docs-images/91/105641694/images/7-0.jpg "Debevec, Rendering synthetic objects into real scenes: Bridging traditional and aimagebased graphics with global illumination and high dynamic range photography, in Proc. ACM SIGGRAPH, pp.")
7 5 (1999). [3] M. knecht, et al., Differential instant radiosity for mixed reality, in Proc. ISMAR (21). [4] P. Debevec, Rendering synthetic objects into real scenes: Bridging traditional and aimagebased graphics with global illumination and high dynamic range photography, in Proc. ACM SIGGRAPH, pp (1998). [5] N. derek et al. Light Factorization for Mixed- Frequency Shadows in Augmented Reality, in Proc. ISMAR (211). [6], 41 SIG1(CVIM1) pp.31-4(2). [7] Gillm R.E, W. Murray M.H. Wright, Practical Optimization, Academic Press, London, UK(1981). [8] Coleman, R.F, Y. Li, A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on Some of the Variables, SIAM Journal on Optimization, 6, 4, pp (1996). [9], CVIM pp.21-28(22).
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1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,
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1,a) 2 2 2011 10 21, 2012 5 12 A Study of Simulating Log-aesthetic Curved Surfaces under Various Light Source Environments with Augmented Reality Ryo Hirano 1,a) Toshinobu Harada 2 Kohe Tokoi 2 Received:
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修士論文の和文要旨 研究科 専攻大学院情報理工学研究科情報 通信工学専攻博士前期課程 氏名春田英和学籍番号 1231074 論文題目 さわれる拡張現実感システムの検討 要 旨 本研究では,AR(Augmented Reality,AR) と様々な入力デバイスを用いた 3DCG モデリングシステムを実装し, さらに物理エンジンと組み合わせることで, さわれる拡張現実感 (AR) システムの有効性を確認した.
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高速流体力学 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/067361 このサンプルページの内容は, 第 1 版発行時のものです. i 20 1999 3 2 2010 5 ii 1 1 1.1 1 1.2 4 9 2 10 2.1 10 2.2 12 2.3 13 2.4 13 2.5
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1 ( 12 11 44 7 20 10 10 1 1 ( ( 2 10 46 11 10 10 5 8 3 2 6 9 47 2 3 48 4 2 2 ( 97 12 ) 97 12 -Spencer modulus moduli (modulus of elasticity) modulus (le) module modulus module 4 b θ a q φ p 1: 3 (le) module
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16 1050026 1050042 1 2 1 1.1 3 1.2 3 1.3 3 2 2.1 4 2.2 4 2.2.1 5 2.2.2 5 2.3 7 2.3.1 1Basic 7 2.3.2 2 8 2.3.3 3 9 2.3.4 4window size 10 2.3.5 5 11 3 3.1 12 3.2 CCF 1 13 3.3 14 3.4 2 15 3.5 3 17 20 20 20
grad φ(p ) φ P grad φ(p ) p P p φ P p l t φ l t = 0 g (0) g (0) (31) grad φ(p ) p grad φ φ (P, φ(p )) xy (x, y) = (ξ(t), η(t)) ( )
2 9 2 5 2.2.3 grad φ(p ) φ P grad φ(p ) p P p φ P p l t φ l t = g () g () (3) grad φ(p ) p grad φ φ (P, φ(p )) y (, y) = (ξ(t), η(t)) ( ) ξ (t) (t) := η (t) grad f(ξ(t), η(t)) (t) g(t) := f(ξ(t), η(t))
II No.01 [n/2] [1]H n (x) H n (x) = ( 1) r n! r!(n 2r)! (2x)n 2r. r=0 [2]H n (x) n,, H n ( x) = ( 1) n H n (x). [3] H n (x) = ( 1) n dn x2 e dx n e x2
![II No.01 [n/2] [1]H n (x) H n (x) = ( 1) r n! r!(n 2r)! (2x)n 2r. r=0 [2]H n (x) n,, H n ( x) = ( 1) n H n (x). [3] H n (x) = ( 1) n dn x2 e dx n e x2](/thumbs/101/149159646.jpg "II No.01 [n/2] [1]H n (x) H n (x) = ( 1) r n! r!(n 2r)! (2x)n 2r. r=0 [2]H n (x) n,, H n ( x) = ( 1) n H n (x). [3] H n (x) = ( 1) n dn x2 e dx n e x2")
II No.1 [n/] [1]H n x) H n x) = 1) r n! r!n r)! x)n r r= []H n x) n,, H n x) = 1) n H n x) [3] H n x) = 1) n dn x e dx n e x [4] H n+1 x) = xh n x) nh n 1 x) ) d dx x H n x) = H n+1 x) d dx H nx) = nh
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38 5 Cauchy.,,,,., σ.,, 3,,. 5.1 Cauchy (a) (b) (a) (b) 5.1: 5.1. Cauchy 39 F Q Newton F F F Q F Q 5.2: n n ds df n ( 5.1). df n n df(n) df n, t n. t n = df n (5.1) ds 40 5 Cauchy t l n mds df n 5.3: t
MH MH 9.50 8.50 9.40 8.40 9.30 9.20 8.30 9.10 9.00 8.20 8.90 8.80 8.10 8.70 8.60 8.00 8.50 7.90 8.40 8.30 7.80 8.20 8.10 7.70 8.00 7.60 7.90 7.80 7.50 7.70 7.60 7.40 1 7.50 7.40 7.30 2 7.30 7.20 7.20