三维网格模型的骨架抽取

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要立蒙
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4 Abstract Ths thess dscusses the method of construct skeleton for 3D trangle mesh. Skeleton s used n buldng realstc anmaton, shape analyss, mesh generaton, path plannng, feature recognton, and so on. We hope to construct skeleton based on 3D trangle mesh especally for anmaton and model search. Ths skeleton needn t to be as accurate as Medal Axs, and need less tme to construct. We brng out our skeleton constructon method based on Progressve Mesh. No complcated structures are nvolved n our method. At the same tme, t s automated and smpler to mplement. It s also robust to nose and holes of 3D data. Ths thess conssts of fve parts. Frst, the applcaton of skeleton for 3D model and man steps to construct skeleton are ntroduced. Next, basc defnton of skeleton and related works are gven. 3D data acquston and range data regstraton and ntegraton are the man content of the thrd part. In the fourth part, skeleton constructon method s descrbed concsely. Dscusson s the fnal of ths thess. Key Word: skeleton, medal axs, progressve mesh, I-K skeleton, trangular mesh 4

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23 A = p } B = p } = 1,2,..., N ' { p = Rp + T + D R T R T D { ' 23

24 e = N = 1 p ( Rp ' + T ) 2 R p ' p q } q } q { { ' ' ' ' = p p q = p p N H = = 1 q ' q T H T = UΛV Λ X T X = VU X X R X X N Λ λ 1 > λ2 > λ3 = 0 H T T T H = λ1u1v1 + λ2u2v2 + 0 u3v3 u U V v H v u3 3 T X = VU e X ' ' = V U T V ' = v, v, v ] [

25 X X X ' A B e X M S = S S xx yx zx S S S xy yy zy S S S xz yz zz n S = x x S = x y x, q x xx = 1 n q, ' q, xy = 1 q, q, q M N M ( S N = xx + S S S S yz zx xy yy S S S + S zy xz yx zz ) ( S xx S yz S S S xy zx S yy S + S zy S yx xz zz ) ( S S S xx S zx xy + S yz S + S yy + S xz yx S zy zz ) ( S S S S xx xy zx yz S S + S + S yy yx xz zy + S zz ) N e N det( N λi ) = 0 I λ m e m ( N λ I) e = 0 m n < 10) O(n) n m 25

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27 N p 2 f ( T ) = ( Tp q j ) q j T p q q p q N p 2 f ( T ) = ( T p q k k, j ) Q P T 0 j j 27

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29 j d, j w, j j D j = w, j w d, j, j W j = w, j 29

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37 V = ν,..., ν } R 3 { 1 m K R m m 3 K R φ ( K ) φ : R m V R V ( r, g, b) n, n, n ) ( u, v) ( x y z M = ( K, V, D, S) V D f = j, k, l} K d S K ( v, f ) s ( v, f ) { f 37

38 Mˆ M 0 M 0 M ˆ = M n 0 1 Mˆ PM M, M,..., M LOD PM Mˆ n ecol({ v s, vt}) vs vt vs v v, v, v } v, v, v } ν M 0 t M ˆ = M n { s t l { t s r s ( Mˆ M n ecol ) n ecol ecol =... M M. M, < n m 0 0 M M V t = m { v1,..., v 1} 0 + { s, v m + 1} ecol v 0 + M v j ν j 38

39 vsplt( s, l, r, t, A) v v v, v, v } v, v, v } v, v } = 0 { s t l { t s r { s t ν s ν t v v v d v v v S ( v,.), S( v,.), S( v,{ v, v, v }) S v,{ v, v, v }) ( s t l s t l s d { s, t, l } { t, s, r } ( r t s r Mˆ M 0 M 0 vsplt1 vsplt 1... ( M vsplt 0 1 n M n = Mˆ ) 0 vsplt ( s, l, r, A ) ( M,{ vsplt0,..., vsplt n 1}) Mˆ v r t 39

40 M vsplt +1 M M M + 1 vsplt ( s, l, r, A G = ( ν, ν )) M (α ) s m G G 0 α 1 M (0) M M (1) M M vs t, v G (1) = M + 1 M G G ( α) = ( K, V ( α)) M + 1 M M m vs t G ν ( α) j + 1 ( α) ν j + (1 α) ν s 1 ν j = v j = +, j { s, m, j { s, m } + + 1} M M + 1 M 1 M 0 vsplt Mˆ 40

41 Mˆ Mˆ Mˆ vsplt s, l, r ) s ( M ν s ν m M v s v s v m v s ν s M d d v, v, v } v, v, v } { s t l { t s r M m0 = 0 m 0 << n 6 log 2 (n) n vsplt s log ( n ) 5) n (

42 M c vsplt ( s, l, r, A ) { 1 { vs, v, } l v r v s vsplt c,..., vsplt n } Mˆ M ˆ = M n 0 M M ν s E dst 42

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Microsoft Word - 系统建设1.doc

Microsoft Word - 系统建设1.doc 计算机系统应用 http://www.c-s-a.org.cn 20 年第 20 卷第 4 期一种高性能近红外光人脸检测和眼睛定位算法张昌明, 童卫青, 王燕群 ( 华东师范大学计算机科学技术系, 上海 20024) 摘要 : 提出了一种新型的近红外光人脸检测和眼睛