Mapping & Warping shapes Geometry Acquisition Zheng Hanlin Summer Seminar
Papers Bounded Biharmonic Weight for Real-Time Deformation (SIG11) Biharmonic Distance (TOG11) Blended Intrinsic Maps (SIG11) Photo-Inspired Model-Driven 3D Object Modeling (SIG11) Style-Content Separation by Anisotropic Part Scales (SIGA10) L1-Sparse Reconstruction of Sharp Point Set Surfaces (TOG) GlobFit: Consistently Fitting Primitives by Discovering Global Relations (SIG11) Data-Driven Suggestions for Creativity Support in 3D Modeling (SIGA10)
Bounded Biharmonic Weight for Real-Time Deformation Sig11
Authors Alec Jacobson – Ph.D. Candidate
Authors Ilya Baran – Postdoc. – Disney Research in Zurich
Authors Olga Sorkine Assistant Professor ETH Zurich
The Main Idea Shape deformation – Work freely with the most convenient combination of handle types bone cage points
Motivation(Video) Typical flow for deformation – Bind the object to handles (bind time) – Manipulate the handles (pose time) Different handle types have different advantages and disadvantages Design the weights for a linear blending scheme Real-time responce
Motivations Deformation Type Free-formSkeleton- based Generalized barycentric coordinate Advantage Nature control for rigid limbs Provide smooth weights automatically Disadvantage Require regular structure Less convenient for flexible regions Need (nearly) closed cages
Algorithm Linear blending: Affine transformation of handle Hj New position Old position Handle size Weight function Bounded biharmonic weights
Algorithm Bounded biharmonic weights:
Algorithm Bounded biharmonic weights: – Properties: Smoothness Non-negativity Shape-awareness Partition of unity Locality and sparsity No local maxima
Algorithm Bounded v.s. Unbounded
Results & Comparison
Results
Performance
Limitation The optimization is not fast enough – Bind-time This weights do NOT have the linear precision property
Conclusion Unify all popular types of control armatures Intuitive design of real-time blending deformation
Biharmonic Distance TOG11
Authors Yaron LipmanRaif M. Rustamov Thomas Funkhouser
The Main Idea A new distance measure based on the biharmonic differential operator
Motivation The most important properties for a distance – metric – smooth – Locally isotropic – Globally shape-aware – Isometry invariant – Insensitive to noise – Small topology changes – Parameter free – Practical to compute on a discrete mesh – … Does there exist a measure cover all these properties?
Related works Geodesic distance – Not smooth, insensitive to topology Diffusion distance – Not locally isotropic – Not global shape-aware – Depending on parameter Commute-time distance (Graph) – Cannot define on surfaces – Depending on the conformal structure
Algorithm Continuous cases: Biharmonic: Green’s function
Algorithm Discrete cases Can be proved: Conformal discrete laplacian
Results & Comparisons
Applications Function interpolation on surfaces
Applications Surface matching
Performances
Conclusions A novel surface distance – Has good properties
Blended Intrinsic Maps Sig11
Authors Vladimir G. Kim – Ph.D. Candidate – Princeton Univ. – He has Canadian and Kyrgyz citizenships.
Authors Yaron Lipman Thomas Funkhouser
The Main Idea Find the maps between two genus 0 surfaces
Related Works Inter-surface mapping Finding sparse correspondences Iterative closest points Finding dense correspondences Surface embedding Exploring Mobius Transformations
Algorithm Blended map Candidate maps Smooth blending weights
Algorithm Generating maps (candidate conformal maps) Defining confidence weights – How much distorting is induced Finding consistency weights – Lower values for incorrect matches Blend map
More Details Finding Consistency Weights – Objective Function – Similarity measure – Optimizing
Results & Comparisons
Results & Performances
Limitation & Conclusion Limitations: – Not guaranteed to work in case of partial near-isometric matching – Only for genus zero surfaces now An automatic method for finding a map between surfaces (including non-isometric surfaces)
Photo-Inspired Model-Driven 3D Object Modeling Sig11
The Main Idea Modeling – From single photo
Workflow
Algorithm Model-driven object analysis – Part-based retrieval Silhouette-guided structure-preserving deformation – Controller construction – Structure-preserving controller optimization
Algorithm Model-driven object analysis – Part-based retrieval Silhouette-guided structure-preserving deformation – Controller construction – Structure-preserving controller optimization
Results
Limitations Limitations: – Candidate sets: new geometric variations but not new structure – Only considered reflectional symmetry
Future works More effective means of structure modification and editing fine-detailed features Using model-driven approach to allow more reusability More means to inspire the user in creative 3D modeling
Style-Content Separation by Anisotropic Part Scales SigA10
The Main Idea
Workflow
Results
Limitations & Conclusions Limitation – Input set should be in the same semantic class – The initial segmentation should be sufficiently meaningful – The synthesis method limits itself to creating new variations of an existing example model Analyze a set of 3D objects belonging to the same class while exhibiting significant shape variations, particularly in part scale
L1-Sparse Reconstruction of Sharp Point Set Surfaces Haim Avron Tel-Aviv Univ. Andrei Sharf UC-Davis Chen Greif Univ. of British Columbia Daniel Cohen-Or Tel-Aviv Univ. TOG11
Authors Haim Avron – Postdoctoral T.J. Watson Research Center – Research field: Numerical linear algebra High performance computing
Authors Chen Greif – Associate Professor – Scientific Computing Laboratory Department of Computer UBC – Research Interests: Iterative solvers Saddle-point linear systems Preconditioning techniques PageRank
The Main Idea Reconstruction
Motivation L1-sparsity paradigm avoid the pitfalls such as least squares, namely smoothed out error – L2 norm tends to severely penalize outliers and propagate the residual in the objective function uniformly Sharp features – Outliers are not excessively penalized – Objective function is expected to be more concentrated near the sharp features.
Related Works 3D Surface Reconstruction Sparse Signal Reconstruction continuous signalbasis functions
Workflow Orientation Reconstruction Position Reconstruction
More Details Orientation Reconstruction – Assume the surface can be approximated well by local planes
More Details Position Reconstruction Second-Order Cone Problem(SOCP) Slover: CVX [Grant and Boyd 2009]
Results
Results & Comparisons
Performance
Limitation & Conclusion Limitations: – Difficult to correctly project points lying exactly on edge singularities. – High computational cost A l1-sparse approach for reconstruction of point set surface with sharp features
GlobFit: Consistently Fitting Primitives by Discovering Global Relations Sig11
Authors Yangyan Li ( 李扬彦 ) – Ph.D. Candidate – Visual Computing Center of SIAT – Chinese Academy of Sciences Xiaokun Wu ( 吴晓堃 )
Authors Yiorgos Chrysanthou – Associate Professor – Univ. of Syprus – The head of the Graphics the University of Cyprus – His current research interests: real-time rendering visibility, crowd rendering and simulation virtual and augmented reality and applications to cultural heritage.
Authors Andrei Sharf – Computer Science Department – Ben-Gurion Univ. – Research interests: Geometry processing and 3D modeling Interactive techniques Topology, parallel data structures on the GPU Large scale 3D urban modeling
Authors Daniel Cohen-Or Niloy J. Mitra
The Main Idea Recover the global mutual relations
Related Works Surface Reconstruction Feature Detection Reverse engineering …
The Workflow
Main Contributions A global approach to constrain and optimize the local RANSAC based primitives
More Details Greedy v.s. Global
More Details re-RANSAC
Evaluation Synthetic datasets – Compare face normals and distances Scanned datasets
Results
Limitations Noise will make the results bad
Conclusion A method for incorporating global relations for man-made objects.
Data-Driven Suggestions for Creativity Support in 3D Modeling
Authors Siddhartha Chaudhuri – Ph.D. Student – Stanford Univ. – Research area: Richer tools for 3D content creation
Authors Vladlen Koltun – Assistant Prof. – Stanford Univ. – Research area: Computer graphics Interactive techniques
Thanks!