A Diffusion Wavelet Approach For 3 D Model Matching

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A Diffusion Wavelet Approach For 3 D Model Matching

  1. 1. A Diffusion Wavelet Approach for 3-D Model Matching<br />Authors: K.P. Zhu, Y.S. Wong, W.F. Lu, J.Y.H. Fuh<br />Presented by: Raphael Steinberg<br />
  2. 2. Schedule<br />Introduction<br />Diffusion Maps<br />Wavelets and Diffusion Wavelets<br />Fisher’s Discriminant Ratio (FDR)<br />Retrieval Procedure<br />Results<br />Conclusions<br />2<br />
  3. 3. Introduction<br />Currently - A larger than ever number of 3D Models in CAD, computer games, multimedia, molecular biology, computer vision and more<br />There is a need for 3D Retrieval <br />3<br />
  4. 4. Introduction (2)<br />Tagging are not always available or sufficient to describe the model we require<br />Combine topological information with multi-scale properties<br />4<br />
  5. 5. Model Reusability (CAD/Animation)<br />Model Matching<br />Video Retrieval (2.5D/Virtual environments)<br />Ecommerce<br />Correcting defects<br />Efficient Representation<br />Many other uses…<br />Motivation for 3D Retrieval<br />5<br />
  6. 6. Obstacles in Retrieval<br />Partial retrieval - Non-transitive<br />Functional description<br />How to match text tags with vertices and texture?<br />Orthonormal coordinate system<br />6<br />
  7. 7. 7<br />
  8. 8. 3D Model Matching – Prior Art<br />Feature vectors using wavelets to mesh vertices – localized in both space & frequency – <br />Paquet et. al. 2000<br />Random sampling for comparison – Osada et. al. 2001<br />8<br />
  9. 9. Spherical harmonics (SH)<br />Global method in Euclidean space<br />lacks multi-scale analysis <br />Legendre polynomials solve the Laplace equation in Spherical coordinates<br />Vranic et. al. 2001<br />9<br />
  10. 10. Spherical Wavelets (SW)<br />Multi-scale in Euclidean space <br />Lacks connectivity on the manifold<br />Tannenbaum et. al. 2007<br />10<br />
  11. 11. Schedule<br />Introduction<br />Diffusion Maps<br />Wavelets and Diffusion Wavelets<br />Fisher’s Discriminant Ratio (FDR)<br />Retrieval Procedure<br />Results<br />Conclusions<br />11<br />
  12. 12. Diffusion Maps Introduction<br />Originally suggested by Stephan Lafon and R.R. Coifman from Yale Math, circa 2005<br />Many other manifold learning techniques exist<br />Data analysis based on geometric properties of the data set<br />12<br />
  13. 13. Manifold Learning algorithms<br />13<br />MANI - Manifold learning Matlab tool<br />
  14. 14. Diffusion Maps<br />Z<br />Y<br />X<br />Coifman - 2005<br /><ul><li> vi is a feature vector
  15. 15. Contains descriptive information about the 3D model</li></ul>14<br />
  16. 16. Diffusion Maps<br />Assumptions<br /><ul><li> Points are sampled uniformly on the manifold
  17. 17. Smooth manifold (no fractals in our case)
  18. 18. Fixed boundary conditions
  19. 19. Enough points = feature vectors (N→∞)</li></ul>15<br />
  20. 20. Use RBF Gaussian Kernel to choose ε<br />Normalize W to create a Stochastic Matrix<br />Diffusion Maps Algorithm<br />16<br />Lu et. al. 2009<br />
  21. 21. Diffusion Maps algorithm (2)<br />Diffuse by taking higher powers of t <br /> “The diffusion distance is equal to the Euclidean distance in the diffusion map space” , Nadler et. al. 2005<br />Cut manifold according to dominant eigenvalues<br />17<br />
  22. 22. Diffusion Maps Code Example<br />function checker();<br />close all;<br />tetha=2*pi*rand(1,500);<br />z=[cos(tetha);sin(tetha)];<br />figure(1);scatter(z(1,:),z(2,:),&apos;b*&apos;);hold on;<br />N=size(z,2);<br />epsilon=linspace(0.01,.3,10);<br />%epsilon=.3;<br />W=nan(N);<br />summer=nan(1,length(epsilon));<br />for k=1:length(epsilon)<br /> for i=1:N<br /> parfor j=1:N<br /> W(i,j)=exp(-sum((z(:,j)-z(:,i)).^2)/2/epsilon(k));<br /> end<br /> end<br /> summer(k)=sum(sum(W));<br />end<br />figure;scatter(log(epsilon),log(summer));title(&apos;Epsilon - linear region&apos;)<br />p=polyfit(log(epsilon),log(summer),1);<br />d=2*p(1);%manifold dimension<br />M=W*diag(1./sum(W,2));<br />[U V]=svds(M);<br />sync=max(U(:,2));<br />figure(1);scatter(U(:,2)./sync,U(:,3)./sync,&apos;rd&apos;)<br />title(&apos;Original manifold as stars and reconstructed manifold as diamonds&apos;)<br />end<br />18<br />
  23. 23. Schedule<br />Introduction<br />Diffusion Maps<br />Wavelets and Diffusion Wavelets<br />Fisher’s Discriminant Ratio (FDR)<br />Retrieval Procedure<br />Results<br />Conclusions<br />19<br />
  24. 24. Problems with Mesh Simplification<br />20<br />
  25. 25. Wavelets<br />21<br />
  26. 26. Novelty – Diffusion Wavelets<br />Combination of Diffusion Maps and Wavelets<br />Used for non-linear dimensionality reduction<br />Extension of wavelets to the unit circle (just as diffusion maps extends the Fourier transform)<br />22<br />
  27. 27. Diffusion Wavelets Intuition<br />23<br />
  28. 28. Example of Diffusion Wavelets<br />24<br />Wavelet basis ψ(2,2,3)<br />Scaling basis φ(1,1,1)<br />Wavelet basis ψ(4,2,5)<br />Wavelet<br />basis ψ(3,2,3)<br />
  29. 29. Diffusion Wavelets<br />Use an optimization scheme to construct the scaling functions<br />Each scaling function should deal with a single dimension and be orthogonal to the other scaling functions<br />Extension of wavelets to the sphere (or to any other manifold)<br />25<br />
  30. 30. Diffusion Wavelets (2)<br />Better than LOD (Level of Detail - simplifies meshes)<br />Involved algorithm – very few implementations exist<br />26<br />
  31. 31. Wavelet decomposition example<br />27<br />
  32. 32. Wavelet coefficients<br />28<br />Scale 1<br />Scale 2<br />Scale 3<br />Scale 4<br />
  33. 33. Schedule<br />Introduction<br />Diffusion Maps<br />Wavelets and Diffusion Wavelets<br />Fisher’s Discriminant Ratio (FDR)<br />Retrieval Procedure<br />Results<br />Conclusions<br />29<br />
  34. 34. Finding Shape Feature Vectors (X)<br />30<br /><ul><li> Take 1,450 coefficients (wavelet + scaling) at each decomposition level
  35. 35. Increase wavelet coefficient number from 0 to 450 and decreasescaling coefficient number from 1,450 to 1,000</li></li></ul><li>Fisher’s Discriminant Ratio<br />b – between classes<br />w – within class (after wavelet decomposition)<br /> j – scale <br />31<br />
  36. 36. Fisher’s Discriminant Ratio<br />32<br />
  37. 37. IRPR Curve<br />Measure performance – use Princeton University 3D database<br />IRPR – Information Retrieval Precision-Recall<br />33<br />
  38. 38. IRPR Curve<br />m = relevant matches <br />r = # of retrieved models<br />1) Precision = <br /> 2) Recall =<br />34<br />
  39. 39. Schedule<br />Introduction<br />Diffusion Maps<br />Wavelets and Diffusion Wavelets<br />Fisher’s Discriminant Ratio (FDR)<br />Retrieval Procedure<br />Results<br />Conclusions<br />35<br />
  40. 40. 3D Model Retrieval Procedure<br />Compute the diffusionwavelet for each 3D model<br />Obtain the model representing vector X<br />Compute the 2nd order statistics of X for each scale<br />36<br />
  41. 41. 1) Start with a coarsest scale comparison <br />2)Advance up to the finest scale <br />3) Stop on threshold or when finest scale reached<br />* Use a threshold to determine if a model is from a certain class<br />Model Matching Procedure<br />37<br />
  42. 42. Schedule<br />Introduction<br />Diffusion Maps<br />Wavelets and Diffusion Wavelets<br />Fisher’s Discriminant Ratio (FDR)<br />Retrieval Procedure<br />Results<br />Conclusions<br />38<br />
  43. 43. Experimental Results<br />39<br />Differences in scaling levels<br />DW gives better results than SH and SW<br />
  44. 44. Visual Results<br />40<br />
  45. 45. Schedule<br />Introduction<br />Diffusion Maps<br />Wavelets and Diffusion Wavelets<br />Fisher’s Discriminant Ratio (FDR)<br />Retrieval Procedure<br />Results<br />Conclusions<br />41<br />
  46. 46. Authors’ Conclusions<br />Surfaces with sharp peaks, grooves or holes contain high-frequency information which is not addressed by the wavelet multi-resolution (use diffusion wavelet packets instead?)<br />Possible to extend to partial matching<br />DW presents better results than SH and SW<br />42<br />
  47. 47. My Conclusions<br />Paper presents a novel solution<br /><ul><li>Diffusion Wavelets was never used before for 3D Retrieval
  48. 48. Less novel solutions:
  49. 49. IRPR is a common measure in database retrieval
  50. 50. Fischer Discriminant Ratio is a common statistical measure</li></ul>43<br />
  51. 51. My Conclusions (2)<br />Technically sound, feasible<br /><ul><li>Taking available code it seems possible to reconstruct the results
  52. 52. Seems like a reasonable solution to the problem of 3D object retrieval</li></ul>44<br />
  53. 53. My Conclusions (3)<br />The diffusion wavelet part could be explained in more detail<br /><ul><li>Not clear in which way the wavelets are constructed
  54. 54. How are the wavelet functions affected when a new model is inserted?</li></ul>45<br />
  55. 55. My Conclusions (4)<br />Not self-containing but reference papers are exceptionally good<br /><ul><li>Missing explanations about diffusion wavelets</li></ul>46<br />
  56. 56. “Would like to have” (Technical/1)<br /> Non-rigid extensions<br /><ul><li>How would retrieval change if we know the 3D model is non-rigid?
  57. 57. Can we have an extension of Diffusion Wavelets for non-rigid manifolds?</li></ul>47<br />
  58. 58. “Would like to have” (Technical/2) <br /> How to automatically choose the level of decomposition<br />48<br />
  59. 59. “Would like to have” (Technical/3)<br /> An intuitive explanation - why prefer Diffusion Wavelets over Diffusion Wavelet Packets?<br />Wavelet Packets seem to give more information especially in high frequencies…<br />49<br />
  60. 60. “Would like to have” (Technical/4)<br /> Numerical problems of overflow of the FDR - use logarithm instead of inverse?<br />50<br />
  61. 61. “Would like to have” (Presentation/1)<br /> Block diagram of the algorithm<br />51<br />
  62. 62. “Would like to have” (Presentation/2)<br /> Web-based Graphical User Interface<br />52<br />
  63. 63. “Would like to have” (Presentation/3)<br /> Error analysis<br />53<br />
  64. 64. “Would like to have” (Presentation/4)<br /> More explanations on Diffusion Wavelets<br />54<br />
  65. 65. Conclusions<br />Shape retrieval requires multi-scale analysis<br />3D models, like most real-life objects, are embedded in a low dimension manifold<br />Results are robust to noise and to mesh simplifications<br />55<br />
  66. 66. Conclusions (2)<br />Diffusion Wavelets give good retrieval results for 3D objects<br />Possible to extend the proposed method to include texture, sound, smell, elasticity and any other possibly given attribute of the 3D model<br />56<br />
  67. 67. THE END<br />57<br />
  68. 68. References<br />[1]K.P. Zhu, Y.S. Wong, W.F. Lu, J.Y.H. Fuh. , Department of Mechanical Engineering, National University of Singapore “A diffusion wavelet approach for 3-D model matching” Computer Aided Design, Elsevier, Nov. 2008<br />[2] Presentation by R.R. Coifman et. al.<br />[3] J. Lu et. al. “Dominant Texture and Diffusion Distance Manifolds“, Eurographics, Volume 28 ,<br />Issue 2, Pages 667 - 676, Mar. 2009<br />[4] Diffusion waveletsMatlab code:<br />http://www.math.duke.edu/~mauro/diffusionwavelets.html#Code|outline<br />[5] The Princeton Shape Benchmark:<br />http://shape.cs.princeton.edu/benchmark/<br />[6] Nadler, B., Lafon, S., Coifman, R., Kevrekidis, I. “Diffusion maps, spectral clustering and eigenfunctions of Fokker-Planck operators”. <br />[7] Ulrike von Luxburg, “A tutorial on spectral clustering”. Statistical Journal 2007<br />[8] Personal communications with K.P. Zhu<br />[9] MANI - Manifold learning Matlab tool<br />http://www.math.umn.edu/~wittman/mani/<br />[10] Vranic D, Saupe D, Richter J. Tools for 3D-object retrieval: Karhunen-Loeve transform and spherical harmonics. In: Proc. IEEE workshop on multimedia signal processing; 2001. p. 29398.<br />58<br />
  69. 69. References<br />[11] Osada R, Funkhouser T, Chazelle B, Dobkin D. Matching 3D models with shape distributions, In: Proc. shape modeling international. 2001. p. 15466.<br />[12] Laga H, Nakajima M. Statistical spherical wavelet moments for content-based 3D model Retrieval. In: Computer graphics international 2007, CGI. 2007; 2007. p.1-8.<br />[13] Nain D, Haker S, Bobick A, Tannenbaum A. Multiscale 3-D shape representation and segmentation using spherical wavelets. IEEE Transactions on Medical Imaging 2007;26(4), pages 598-618.<br />59<br />

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