Data-Driven Geometry Processing Map Synchronization I. Qixing Huang Nov. 28 th 2018
|
|
- Bernard McCarthy
- 5 years ago
- Views:
Transcription
1 Data-Driven Geometry Processing Map Synchronization I Qixing Huang Nov. 28 th 2018
2 Shape matching Affine
3 Applications Shape reconstruction Transfer information Aggregate information Protein docking
4 Pair-wise matching is unreliable State-of-the-art techniques: Blended intrinsic maps [Kim et al. 11] Learning-based graph matching [Leordeanu et al. 12]
5 5 Piece assembly
6 6 Ambiguous matches
7 Additional data helps
8 Additional data helps Blended intrinsic maps [Kim et al. 11] Intermediate object Composite
9 Cycle-consistency Maps are consistent along cycles Consistent
10 Cycle-consistency Maps are consistent along cycles Inconsistent
11 Cycle-consistency Blended intrinsic maps [Kim et al. 11] Inconsistent Composite
12 Cycle-consistency Blended intrinsic maps [Kim et al. 11] Direct Consistent Composite
13 Input: Joint matching formulation Shapes Pair-wise maps (existing algorithms)
14 Input: Joint matching formulation Shapes Pair-wise maps (existing algorithms) Output: Cycle-consistent Close to the input maps NP-complete [Huber 2002]
15 Outline Three existing approaches Spanning tree based Inconsistent cycle detection Spectral techniques Convex optimization framework
16 Spanning tree based Automatic Three-dimensional Modeling from Reality, PhD thesis, D. Huber, Robotics Institute, Carnegie Mellon University, 2002
17 Spanning tree based
18 Spanning tree based Issue: A single incorrect match can destroy everything
19 Detecting inconsistent cycles large for inconsistent cycles maximize subject to Disambiguating visual relations using loop constraints, C. Zach, M. Klopscjotz, and M. POLLEFEYS, CVPR 10
20 Spectral techniques The resulting soft map From paths of length k Diffusion Multiplication and aggregation of mapping matrices An Optimization Approach for Extracting and Encoding Consistent Maps in a Shape Collection, Q. Huang, G. Zhang, L. Gao, S. Hu, A. Bustcher, L. Guibas, SIGGRAPH ASIA 12
21 Spectral techniques Compute fuzzy correspondence based on diffusion distance in graph represented by initial correspondences Exploring collections of 3d models using fuzzy correspondences, V. G. Kim, W. Li, N. Mitra, S. Diverdi, and T. Funkhouser, SIGGRAPH 12
22 Approaches we have discussed Spanning tree optimization [Huber and Hebert 03] Cons: --- Many parameters to set --- Does it converge? --- Run quickly? Detecting Inconsistent Cycles [Zach et al. 10, Nguyen et al. 11] Spectral techniques [Kim et al.12, Pachauri et al.13]
23 Outline Three existing approaches Spanning tree based Inconsistent cycle detection Spectral techniques Convex optimization framework
24 Convex optimization framework Parameter-free! Near-optimal! Efficient!
25 Basic setting G n objects, each object has m points
26 Matrix representation of maps S S X 12 = X = I m X 12 X 1n X12 T I m X (n 1);n X1n T. X (n 1);n T I m Diagonal blocks are identity matrices Off diagonal blocks are permutation matrices Symmetric
27 Cycle-consistency constraint X º 0 (Positive) semidefiniteness X ij = X T j1 X i1 X = I ṃ h Im X n1 i X T n1
28 Parameterization X = X ii = I m ; 1 i n X ij 1 = 1; X T ij 1 = 1; 1 i < j n X º 0 X 2 f0; 1g nm
29 Parametrizing maps X = X ii = I m ; 1 i n X ij 1 = 1; X T ij 1 = 1; 1 i < j n X º 0 X 0
30 Objective function X input ij L1-norm: P (i;j)2e kx input ij X ij k 1 X ij
31 Convex program minimize subject to P (i;j)2e kx input ij X ij k 1 X ii = I m ; 1 i n X ij 1 = 1; X T ij 1 = 1; 1 i < j n X º 0 X 0 ADMM [Boyd et al.11]
32 Convex program minimize subject to P (i;j)2e kx input ij X ij k 1 X ii = I m ; 1 i n X ij 1 = 1; X T ij 1 = 1; 1 i < j n X º 0 X 0 ADMM [Boyd et al.11]
33 Deterministic guarantee Exact recovery condition: #incorrect corres. per point < algebraic-connectivity(g)/4
34 Complete input graph G 25% incorrect correspondences Worst-case scenario Two clusters of objects of equal size Wrong correspondences between objects of different clusters only (50%)
35 Randomized setting Erdős Rényi model G(n, p obs, p true ) : p obs : the probability that two objects connect; p true : the probability that a pair-wise map is correct; Incorrect maps are random permutations; Theorem: The ground truth maps can be recovered w.h.p if p true > c log2 n p npobs
36 Phase transition Numerical optimization: ADMM [Wen et al. 10, Boyd et al. 11] m = 16 Red: never recovers Blue: always recovers
37 Versus RPCA [Candes et al. 11] X = I ṃ. X T 1n h Im X 1n i SDP RPCA
38 CMU hotel Input: 102 images (30 points per image) RANSAC [Fisher 81] Each image connects with 10 random images SDP Running time: 6m19s (3.2GHZ, single core) Input SDP RPCA Leordeanu et al % 100% 90.1% 94.8%
39 CMU house Input: 110 images (30 points per image) RANSAC [Fisher 81] Each image connects with 10 random images SDP Running time: 7m12s (3.2GHZ, single core) Input SDP RPCA Leordeanu et al % 100% 92.2% 99.8%
40 Constraint set X = I m1 X 12 X 1n X 21 I m2 X 2n X n1 I mn minimize P (i;j)2e kx input ij X ij k 1 X ii = I m ; 1 i n " # m 1 T º 0 1 X X 0 #Distinctive points X ii = I m ; 1 i n X ij 1 = 1; X T ij 1 = 1; 1 i < j n X º 0 X 0 SDP relaxation of MAP [kumar et al. 09]
41 Partial similarity Step I: Estimate m (#distinctive points) -- Gap in the spectrum of the input map matrix (In the same spirit as [Pachauri et al. 13]) Step II: minimize subject to P (i;j)2e X ii = I mi ; 1 i n " # m 1 T º 0 1 X X 0 kx input ij X ij k 1
42 Random model Extended model: an universe of m elements: For each set S i, each point s is included in S i with probability p set ; p obs : the probability that two objects connect p true : the probability that a pair-wise map is correct Incorrect maps satisfy
43 Similar theoretical guarantee Theorem: The size of the universe m and the ground truth maps can be recovered w.h.p if p true > C 1 log 2 n p npobs p 2 set
44 Chair 16 images Clustered SIFT features + RANSAC ( points per image) SDP Running time: 2m19s (3.2GHZ, single core) Input output
45 Building Input 16 images Clustered SIFT features + RANSAC ( points per image) Output SDP Running time: 5m07s (3.2GHZ, single core)
46 3D Benchmark Armadillo, Fish, Fourleg, Human and Hand 20 objects, 128 points per object Blended intrinsic maps as input [Kim et al. 11]
47 Next lecture Rotations [Wang and Singer 13] Functional maps [Huang et al. 14]
48 Next lecture Super-objects = = =
49 References Automatic Three-dimensional Modeling from Reality, PhD thesis, D. Huber, Robotics Institute, Carnegie Mellon University, 2002 Reassembling fractured objects by geometric matching. Q. Huang, S. Flory, N. Gelfand, M. Hofer, and H. Pottmann, SIGGRAPH 06 Disambiguating visual relations using loop constraints, C. Zach, M. Klopscjotz, and M. POLLEFEYS, CVPR 10 An optimization approach to improving collections of shape maps, A. Nguyen, M. Ben- Chen, K. Welnicka, Y. Ye, and L. Guibas, SGP 11 Exploring collections of 3d models using fuzzy correspondences, V. G. Kim, W. Li, N. Mitra, S. Diverdi, and T. Funkhouser, SIGGRAPH 12 An Optimization Approach for Extracting and Encoding Consistent Maps in a Shape Collection, Q. Huang, G. Zhang, L. Gao, S. Hu, A. Bustcher, L. Guibas, SIGGRAPH ASIA 12 Consistent Shape Maps via Semidefinite Programming, Q. Huang and L. Guibas, SGP 13 Matching Partially Similar Objects via Matrix Completion, Y. Chen, L. Guibas and Q. Huang, ICML 14
Data-Driven Shape Analysis --- Joint Shape Matching I. Qi-xing Huang Stanford University
Data-Driven Shape Analysis --- Joint Shape Matching I Qi-xing Huang Stanford University 1 Shape matching Affine Applications Shape reconstruction Transfer information Aggregate information Protein docking
More informationMulti-View Matching & Mesh Generation. Qixing Huang Feb. 13 th 2017
Multi-View Matching & Mesh Generation Qixing Huang Feb. 13 th 2017 Geometry Reconstruction Pipeline RANSAC --- facts Sampling Feature point detection [Gelfand et al. 05, Huang et al. 06] Correspondences
More informationExploring Collections of 3D Models using Fuzzy Correspondences
Exploring Collections of 3D Models using Fuzzy Correspondences Vladimir G. Kim Wilmot Li Niloy J. Mitra Princeton University Adobe UCL Stephen DiVerdi Adobe Thomas Funkhouser Princeton University Motivating
More informationFinding Structure in Large Collections of 3D Models
Finding Structure in Large Collections of 3D Models Vladimir Kim Adobe Research Motivation Explore, Analyze, and Create Geometric Data Real Virtual Motivation Explore, Analyze, and Create Geometric Data
More informationShape Co-analysis. Daniel Cohen-Or. Tel-Aviv University
Shape Co-analysis Daniel Cohen-Or Tel-Aviv University 1 High-level Shape analysis [Fu et al. 08] Upright orientation [Mehra et al. 08] Shape abstraction [Kalograkis et al. 10] Learning segmentation [Mitra
More informationJoint Shape Segmentation
Joint Shape Segmentation Motivations Structural similarity of segmentations Extraneous geometric clues Single shape segmentation [Chen et al. 09] Joint shape segmentation [Huang et al. 11] Motivations
More informationStructured Light II. Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov
Structured Light II Johannes Köhler Johannes.koehler@dfki.de Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov Introduction Previous lecture: Structured Light I Active Scanning Camera/emitter
More information3D Computer Vision. Structured Light II. Prof. Didier Stricker. Kaiserlautern University.
3D Computer Vision Structured Light II Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 Introduction
More informationCS233: The Shape of Data Handout # 3 Geometric and Topological Data Analysis Stanford University Wednesday, 9 May 2018
CS233: The Shape of Data Handout # 3 Geometric and Topological Data Analysis Stanford University Wednesday, 9 May 2018 Homework #3 v4: Shape correspondences, shape matching, multi-way alignments. [100
More informationDiscovering Similarities in 3D Data
Discovering Similarities in 3D Data Vladimir Kim, Tianqiang Liu, Sid Chaudhuri, Steve Diverdi, Wilmot Li, Niloy Mitra, Yaron Lipman, Thomas Funkhouser Motivation 3D data is widely available Medicine Mechanical
More informationStructure-oriented Networks of Shape Collections
Structure-oriented Networks of Shape Collections Noa Fish 1 Oliver van Kaick 2 Amit Bermano 3 Daniel Cohen-Or 1 1 Tel Aviv University 2 Carleton University 3 Princeton University 1 pplementary material
More informationLecture 10: Multi view geometry
Lecture 10: Multi view geometry Professor Fei Fei Li Stanford Vision Lab 1 What we will learn today? Stereo vision Correspondence problem (Problem Set 2 (Q3)) Active stereo vision systems Structure from
More informationVisual Representations for Machine Learning
Visual Representations for Machine Learning Spectral Clustering and Channel Representations Lecture 1 Spectral Clustering: introduction and confusion Michael Felsberg Klas Nordberg The Spectral Clustering
More informationImproving Shape retrieval by Spectral Matching and Meta Similarity
1 / 21 Improving Shape retrieval by Spectral Matching and Meta Similarity Amir Egozi (BGU), Yosi Keller (BIU) and Hugo Guterman (BGU) Department of Electrical and Computer Engineering, Ben-Gurion University
More informationCorrespondence. CS 468 Geometry Processing Algorithms. Maks Ovsjanikov
Shape Matching & Correspondence CS 468 Geometry Processing Algorithms Maks Ovsjanikov Wednesday, October 27 th 2010 Overall Goal Given two shapes, find correspondences between them. Overall Goal Given
More informationGeneralized Transitive Distance with Minimum Spanning Random Forest
Generalized Transitive Distance with Minimum Spanning Random Forest Author: Zhiding Yu and B. V. K. Vijaya Kumar, et al. Dept of Electrical and Computer Engineering Carnegie Mellon University 1 Clustering
More informationMultiple View Geometry
Multiple View Geometry CS 6320, Spring 2013 Guest Lecture Marcel Prastawa adapted from Pollefeys, Shah, and Zisserman Single view computer vision Projective actions of cameras Camera callibration Photometric
More informationarxiv: v1 [cs.ds] 22 Nov 2016
Distributable Consistent Multi-Graph Matching Nan Hu Boris Thibert Leonidas Guibas Stanford University, Stanford, CA, USA pkuhunan@gmail.com, boris.thibert@imag.fr, guibas@cs.stanford.edu November 3, 6
More informationLecture 10: Multi-view geometry
Lecture 10: Multi-view geometry Professor Stanford Vision Lab 1 What we will learn today? Review for stereo vision Correspondence problem (Problem Set 2 (Q3)) Active stereo vision systems Structure from
More informationDepth Estimation Using Collection of Models
Depth Estimation Using Collection of Models Karankumar Thakkar 1, Viral Borisagar 2 PG scholar 1, Assistant Professor 2 Computer Science & Engineering Department 1, 2 Government Engineering College, Sector
More informationCompu&ng Correspondences in Geometric Datasets. 4.2 Symmetry & Symmetriza/on
Compu&ng Correspondences in Geometric Datasets 4.2 Symmetry & Symmetriza/on Symmetry Invariance under a class of transformations Reflection Translation Rotation Reflection + Translation + global vs. partial
More informationStructure from motion
Structure from motion Structure from motion Given a set of corresponding points in two or more images, compute the camera parameters and the 3D point coordinates?? R 1,t 1 R 2,t R 2 3,t 3 Camera 1 Camera
More informationDiscrete Optimization of Ray Potentials for Semantic 3D Reconstruction
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction Marc Pollefeys Joined work with Nikolay Savinov, Christian Haene, Lubor Ladicky 2 Comparison to Volumetric Fusion Higher-order ray
More informationStructure from Motion. Introduction to Computer Vision CSE 152 Lecture 10
Structure from Motion CSE 152 Lecture 10 Announcements Homework 3 is due May 9, 11:59 PM Reading: Chapter 8: Structure from Motion Optional: Multiple View Geometry in Computer Vision, 2nd edition, Hartley
More informationDetecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference Minh Dao 1, Xiang Xiang 1, Bulent Ayhan 2, Chiman Kwan 2, Trac D. Tran 1 Johns Hopkins Univeristy, 3400
More informationLimitations of Matrix Completion via Trace Norm Minimization
Limitations of Matrix Completion via Trace Norm Minimization ABSTRACT Xiaoxiao Shi Computer Science Department University of Illinois at Chicago xiaoxiao@cs.uic.edu In recent years, compressive sensing
More informationOn Order-Constrained Transitive Distance
On Order-Constrained Transitive Distance Author: Zhiding Yu and B. V. K. Vijaya Kumar, et al. Dept of Electrical and Computer Engineering Carnegie Mellon University 1 Clustering Problem Important Issues:
More informationCS 395T Numerical Optimization for Graphics and AI (3D Vision) Qixing Huang August 29 th 2018
CS 395T Numerical Optimization for Graphics and AI (3D Vision) Qixing Huang August 29 th 2018 3D Vision Understanding geometric relations between images and the 3D world between images Obtaining 3D information
More informationAlgorithms for 3D Isometric Shape Correspondence
Algorithms for 3D Isometric Shape Correspondence Yusuf Sahillioğlu Computer Eng. Dept., Koç University, Istanbul, Turkey (PhD) Computer Eng. Dept., METU, Ankara, Turkey (Asst. Prof.) 2 / 53 Problem Definition
More informationC280, Computer Vision
C280, Computer Vision Prof. Trevor Darrell trevor@eecs.berkeley.edu Lecture 11: Structure from Motion Roadmap Previous: Image formation, filtering, local features, (Texture) Tues: Feature-based Alignment
More informationBilevel Sparse Coding
Adobe Research 345 Park Ave, San Jose, CA Mar 15, 2013 Outline 1 2 The learning model The learning algorithm 3 4 Sparse Modeling Many types of sensory data, e.g., images and audio, are in high-dimensional
More informationEfficient regularization of functional map computations
Efficient regularization of functional map computations Flows, mappings and shapes VMVW03 Workshop December 12 th, 2017 Maks Ovsjanikov Joint with: E. Corman, D. Nogneng, R. Huang, M. Ben-Chen, J. Solomon,
More informationMirror-symmetry in images and 3D shapes
Journées de Géométrie Algorithmique CIRM Luminy 2013 Mirror-symmetry in images and 3D shapes Viorica Pătrăucean Geometrica, Inria Saclay Symmetry is all around... In nature: mirror, rotational, helical,
More informationObject Matching Using A Locally Affine Invariant and Linear Programming Techniques
1 Object Matching Using A Locally Affine Invariant and Linear Programming Techniques Hongsheng Li, Xiaolei Huang, Member, IEEE, and Lei He, Hongsheng Li and Xiaolei Huang are with the Department of Computer
More informationHow Much Geometry Lies in The Laplacian?
How Much Geometry Lies in The Laplacian? Encoding and recovering the discrete metric on triangle meshes Distance Geometry Workshop in Bad Honnef, November 23, 2017 Maks Ovsjanikov Joint with: E. Corman,
More informationSolving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality Song Mei Stanford University July 8, 2017 Joint work with Theodor Misiakiewicz, Andrea Montanari, and Roberto I. Oliveira
More informationComputing and Processing Correspondences with Functional Maps
Computing and Processing Correspondences with Functional Maps SIGGRAPH 2017 course Maks Ovsjanikov, Etienne Corman, Michael Bronstein, Emanuele Rodolà, Mirela Ben-Chen, Leonidas Guibas, Frederic Chazal,
More informationThe correspondence problem. A classic problem. A classic problem. Deformation-Drive Shape Correspondence. Fundamental to geometry processing
The correspondence problem Deformation-Drive Shape Correspondence Hao (Richard) Zhang 1, Alla Sheffer 2, Daniel Cohen-Or 3, Qingnan Zhou 2, Oliver van Kaick 1, and Andrea Tagliasacchi 1 July 3, 2008 1
More informationShape from Silhouettes I CV book Szelisky
Shape from Silhouettes I CV book Szelisky 11.6.2 Guido Gerig CS 6320, Spring 2012 (slides modified from Marc Pollefeys UNC Chapel Hill, some of the figures and slides are adapted from M. Pollefeys, J.S.
More informationBIL Computer Vision Apr 16, 2014
BIL 719 - Computer Vision Apr 16, 2014 Binocular Stereo (cont d.), Structure from Motion Aykut Erdem Dept. of Computer Engineering Hacettepe University Slide credit: S. Lazebnik Basic stereo matching algorithm
More informationLearning-based Neuroimage Registration
Learning-based Neuroimage Registration Leonid Teverovskiy and Yanxi Liu 1 October 2004 CMU-CALD-04-108, CMU-RI-TR-04-59 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract
More information3D Modeling using multiple images Exam January 2008
3D Modeling using multiple images Exam January 2008 All documents are allowed. Answers should be justified. The different sections below are independant. 1 3D Reconstruction A Robust Approche Consider
More informationUnsupervised Learning for Graph Matching
Unsupervised Learning for Graph Matching Marius Leordeanu Carnegie Mellon University Pittsburgh, PA mleordea@andrew.cmu.edu Martial Hebert Carnegie Mellon University Pittsburgh, PA hebert@ri.cmu.edu Abstract
More informationLearning 3D Part Detection from Sparsely Labeled Data: Supplemental Material
Learning 3D Part Detection from Sparsely Labeled Data: Supplemental Material Ameesh Makadia Google New York, NY 10011 makadia@google.com Mehmet Ersin Yumer Carnegie Mellon University Pittsburgh, PA 15213
More informationJavad Lavaei. Department of Electrical Engineering Columbia University
Graph Theoretic Algorithm for Nonlinear Power Optimization Problems Javad Lavaei Department of Electrical Engineering Columbia University Joint work with: Ramtin Madani, Ghazal Fazelnia and Abdulrahman
More informationUnsupervised Co-Segmentation of 3D Shapes via Affinity Aggregation Spectral Clustering
Unsupervised Co-Segmentation of 3D Shapes via Affinity Aggregation Spectral Clustering Zizhao Wu a, Yunhai Wang b, Ruyang Shou a, Baoquan Chen b, Xinguo Liu a Corresponding author: cloudseawang@gmail.com
More informationDetecting Object Instances Without Discriminative Features
Detecting Object Instances Without Discriminative Features Edward Hsiao June 19, 2013 Thesis Committee: Martial Hebert, Chair Alexei Efros Takeo Kanade Andrew Zisserman, University of Oxford 1 Object Instance
More informationThe end of affine cameras
The end of affine cameras Affine SFM revisited Epipolar geometry Two-view structure from motion Multi-view structure from motion Planches : http://www.di.ens.fr/~ponce/geomvis/lect3.pptx http://www.di.ens.fr/~ponce/geomvis/lect3.pdf
More informationFinding Surface Correspondences With Shape Analysis
Finding Surface Correspondences With Shape Analysis Sid Chaudhuri, Steve Diverdi, Maciej Halber, Vladimir Kim, Yaron Lipman, Tianqiang Liu, Wilmot Li, Niloy Mitra, Elena Sizikova, Thomas Funkhouser Motivation
More informationStructured Light II. Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov
Structured Light II Johannes Köhler Johannes.koehler@dfki.de Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov Introduction Previous lecture: Structured Light I Active Scanning Camera/emitter
More informationSurfNet: Generating 3D shape surfaces using deep residual networks-supplementary Material
SurfNet: Generating 3D shape surfaces using deep residual networks-supplementary Material Ayan Sinha MIT Asim Unmesh IIT Kanpur Qixing Huang UT Austin Karthik Ramani Purdue sinhayan@mit.edu a.unmesh@gmail.com
More informationNon-linear dimension reduction
Sta306b May 23, 2011 Dimension Reduction: 1 Non-linear dimension reduction ISOMAP: Tenenbaum, de Silva & Langford (2000) Local linear embedding: Roweis & Saul (2000) Local MDS: Chen (2006) all three methods
More informationComplex Non-Rigid Motion 3D Reconstruction by Union of Subspaces
Complex Non-Rigid Motion 3D Reconstruction by Union of Subspaces Yingying Zhu University of Queensland Dong Huang Carnegie Mellon University zhuyingying2@gmail.com dghuang@andrew. Fernando De La Torre
More informationTwo-View Geometry (Course 23, Lecture D)
Two-View Geometry (Course 23, Lecture D) Jana Kosecka Department of Computer Science George Mason University http://www.cs.gmu.edu/~kosecka General Formulation Given two views of the scene recover the
More informationStereo and Epipolar geometry
Previously Image Primitives (feature points, lines, contours) Today: Stereo and Epipolar geometry How to match primitives between two (multiple) views) Goals: 3D reconstruction, recognition Jana Kosecka
More informationShape from Silhouettes I
Shape from Silhouettes I Guido Gerig CS 6320, Spring 2013 Credits: Marc Pollefeys, UNC Chapel Hill, some of the figures and slides are also adapted from J.S. Franco, J. Matusik s presentations, and referenced
More informationRegistration of Dynamic Range Images
Registration of Dynamic Range Images Tan-Chi Ho 1,2 Jung-Hong Chuang 1 Wen-Wei Lin 2 Song-Sun Lin 2 1 Department of Computer Science National Chiao-Tung University 2 Department of Applied Mathematics National
More informationPosture Invariant Surface Description and Feature Extraction
Posture Invariant Surface Description and Feature Extraction Stefanie Wuhrer 1 Zouhour Ben Azouz 2 Chang Shu 1 Abstract We propose a posture invariant surface descriptor for triangular meshes. Using intrinsic
More informationSupplementary Material : Partial Sum Minimization of Singular Values in RPCA for Low-Level Vision
Supplementary Material : Partial Sum Minimization of Singular Values in RPCA for Low-Level Vision Due to space limitation in the main paper, we present additional experimental results in this supplementary
More informationLow Rank Representation Theories, Algorithms, Applications. 林宙辰 北京大学 May 3, 2012
Low Rank Representation Theories, Algorithms, Applications 林宙辰 北京大学 May 3, 2012 Outline Low Rank Representation Some Theoretical Analysis Solution by LADM Applications Generalizations Conclusions Sparse
More information3D Deep Learning
3D Deep Learning Tutorial@CVPR2017 Hao Su (UCSD) Leonidas Guibas (Stanford) Michael Bronstein (Università della Svizzera Italiana) Evangelos Kalogerakis (UMass) Jimei Yang (Adobe Research) Charles Qi (Stanford)
More informationCS 4495 Computer Vision A. Bobick. Motion and Optic Flow. Stereo Matching
Stereo Matching Fundamental matrix Let p be a point in left image, p in right image l l Epipolar relation p maps to epipolar line l p maps to epipolar line l p p Epipolar mapping described by a 3x3 matrix
More information3D Deep Learning on Geometric Forms. Hao Su
3D Deep Learning on Geometric Forms Hao Su Many 3D representations are available Candidates: multi-view images depth map volumetric polygonal mesh point cloud primitive-based CAD models 3D representation
More informationA Spectral Technique for Correspondence Problems Using Pairwise Constraints
A Spectral Technique for Correspondence Problems Using Pairwise Constraints Marius Leordeanu Martial Hebert The Robotics Institute Carnegie Mellon University Pittsburgh, PA 523 Abstract We present an efficient
More informationTopology-Invariant Similarity and Diffusion Geometry
1 Topology-Invariant Similarity and Diffusion Geometry Lecture 7 Alexander & Michael Bronstein tosca.cs.technion.ac.il/book Numerical geometry of non-rigid shapes Stanford University, Winter 2009 Intrinsic
More information3D Reconstruction of a Hopkins Landmark
3D Reconstruction of a Hopkins Landmark Ayushi Sinha (461), Hau Sze (461), Diane Duros (361) Abstract - This paper outlines a method for 3D reconstruction from two images. Our procedure is based on known
More informationBig-data Clustering: K-means vs K-indicators
Big-data Clustering: K-means vs K-indicators Yin Zhang Dept. of Computational & Applied Math. Rice University, Houston, Texas, U.S.A. Joint work with Feiyu Chen & Taiping Zhang (CQU), Liwei Xu (UESTC)
More informationResearch Article Polygon Morphing and Its Application in Orebody Modeling
Mathematical Problems in Engineering Volume 212, Article ID 732365, 9 pages doi:1.1155/212/732365 Research Article Polygon Morphing and Its Application in Orebody Modeling Hacer İlhan and Haşmet Gürçay
More informationDeformable Segmentation using Sparse Shape Representation. Shaoting Zhang
Deformable Segmentation using Sparse Shape Representation Shaoting Zhang Introduction Outline Our methods Segmentation framework Sparse shape representation Applications 2D lung localization in X-ray 3D
More information1-2 Feature-Based Image Mosaicing
MVA'98 IAPR Workshop on Machine Vision Applications, Nov. 17-19, 1998, Makuhari, Chibq Japan 1-2 Feature-Based Image Mosaicing Naoki Chiba, Hiroshi Kano, Minoru Higashihara, Masashi Yasuda, and Masato
More informationStructure from Motion
Structure from Motion Outline Bundle Adjustment Ambguities in Reconstruction Affine Factorization Extensions Structure from motion Recover both 3D scene geoemetry and camera positions SLAM: Simultaneous
More informationStructure from Motion CSC 767
Structure from Motion CSC 767 Structure from motion Given a set of corresponding points in two or more images, compute the camera parameters and the 3D point coordinates?? R,t R 2,t 2 R 3,t 3 Camera??
More informationImproved Functional Mappings via Product Preservation
Improved Functional Mappings via Product Preservation Workshop: Imaging and Vision March 15 th, 2018 Maks Ovsjanikov Joint with: D. Nogneng, Simone Melzi, Emanuele Rodolà, Umberto Castellani, Michael Bronstein
More informationK-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors
K-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors Shao-Tzu Huang, Chen-Chien Hsu, Wei-Yen Wang International Science Index, Electrical and Computer Engineering waset.org/publication/0007607
More informationPractical Camera Auto Calibration using Semidefinite Programming
Practical Camera Auto Calibration using Semidefinite Programming Motilal Agrawal SRI International 333 Ravenswood Ave. Menlo Park, CA 94025 agrawal@ai.sri.com Abstract We describe a novel approach to the
More informationStructure from motion
Structure from motion Structure from motion Given a set of corresponding points in two or more images, compute the camera parameters and the 3D point coordinates?? R 1,t 1 R 2,t 2 R 3,t 3 Camera 1 Camera
More informationConic Duality. yyye
Conic Linear Optimization and Appl. MS&E314 Lecture Note #02 1 Conic Duality Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/
More informationAn incomplete survey of matrix completion. Benjamin Recht Department of Computer Sciences University of Wisconsin-Madison
An incomplete survey of matrix completion Benjamin Recht Department of Computer Sciences University of Wisconsin-Madison Abstract Setup: Matrix Completion M = M ij known for black cells M ij unknown for
More informationTrajectory Optimization
Trajectory Optimization Jane Li Assistant Professor Mechanical Engineering & Robotics Engineering http://users.wpi.edu/~zli11 Recap We heard about RRT*, a sampling-based planning in high-dimensional cost
More informationLecture 17 Sparse Convex Optimization
Lecture 17 Sparse Convex Optimization Compressed sensing A short introduction to Compressed Sensing An imaging perspective 10 Mega Pixels Scene Image compression Picture Why do we compress images? Introduction
More informationFunctional Maps: A Flexible Representation of Maps Between Shapes
Functional aps: A Flexible Representation of aps Between Shapes aks Ovsjanikov irela Ben-Chen Justin Solomon Adrian Butscher Leonidas Guibas LIX, École Polytechnique Stanford University Figure 1: Horse
More informationCamera Calibration. Schedule. Jesus J Caban. Note: You have until next Monday to let me know. ! Today:! Camera calibration
Camera Calibration Jesus J Caban Schedule! Today:! Camera calibration! Wednesday:! Lecture: Motion & Optical Flow! Monday:! Lecture: Medical Imaging! Final presentations:! Nov 29 th : W. Griffin! Dec 1
More informationA Geometric Analysis of Subspace Clustering with Outliers
A Geometric Analysis of Subspace Clustering with Outliers Mahdi Soltanolkotabi and Emmanuel Candés Stanford University Fundamental Tool in Data Mining : PCA Fundamental Tool in Data Mining : PCA Subspace
More informationCluster algebras and infinite associahedra
Cluster algebras and infinite associahedra Nathan Reading NC State University CombinaTexas 2008 Coxeter groups Associahedra and cluster algebras Sortable elements/cambrian fans Infinite type Much of the
More informationPhase Transitions in Semidefinite Relaxations
Phase Transitions in Semidefinite Relaxations Andrea Montanari [with Adel Javanmard, Federico Ricci-Tersenghi, Subhabrata Sen] Stanford University April 5, 2016 Andrea Montanari (Stanford) SDP Phase Transitions
More informationComputer Vision I - Appearance-based Matching and Projective Geometry
Computer Vision I - Appearance-based Matching and Projective Geometry Carsten Rother 05/11/2015 Computer Vision I: Image Formation Process Roadmap for next four lectures Computer Vision I: Image Formation
More informationMulti-Image Matching via Fast Alternating Minimization
Multi-Image Matching via Fast Alternating Minimization Xiaowei Zhou, Menglong Zhu, Kostas Daniilidis GRASP Laboratory, University of Pennsylvania {xiaowz,menglong,kostas}@seas.upenn.edu Abstract In this
More informationNon-rigid shape correspondence by matching semi-local spectral features and global geodesic structures
Non-rigid shape correspondence by matching semi-local spectral features and global geodesic structures Anastasia Dubrovina Technion Israel Institute of Technology Introduction Correspondence detection
More informationLarge-Scale Face Manifold Learning
Large-Scale Face Manifold Learning Sanjiv Kumar Google Research New York, NY * Joint work with A. Talwalkar, H. Rowley and M. Mohri 1 Face Manifold Learning 50 x 50 pixel faces R 2500 50 x 50 pixel random
More informationCombinatorial Selection and Least Absolute Shrinkage via The CLASH Operator
Combinatorial Selection and Least Absolute Shrinkage via The CLASH Operator Volkan Cevher Laboratory for Information and Inference Systems LIONS / EPFL http://lions.epfl.ch & Idiap Research Institute joint
More informationJustin Solomon MIT, Spring
Justin Solomon MIT, Spring 2017 http://www.gogeometry.com Instructor: Justin Solomon Email: jsolomon@mit.edu Office: 32-D460 Office hours: Wednesdays, 1pm-3pm Geometric Data Processing
More informationUnit 3 Multiple View Geometry
Unit 3 Multiple View Geometry Relations between images of a scene Recovering the cameras Recovering the scene structure http://www.robots.ox.ac.uk/~vgg/hzbook/hzbook1.html 3D structure from images Recover
More informationCSE 167: Introduction to Computer Graphics Lecture #13: Curves. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2017
CSE 167: Introduction to Computer Graphics Lecture #13: Curves Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2017 Announcements Project 4 due Monday Nov 27 at 2pm Next Tuesday:
More informationData-driven Depth Inference from a Single Still Image
Data-driven Depth Inference from a Single Still Image Kyunghee Kim Computer Science Department Stanford University kyunghee.kim@stanford.edu Abstract Given an indoor image, how to recover its depth information
More informationLearning Probabilistic Models from Collections of 3D Meshes
Learning Probabilistic Models from Collections of 3D Meshes Sid Chaudhuri, Steve Diverdi, Matthew Fisher, Pat Hanrahan, Vladimir Kim, Wilmot Li, Niloy Mitra, Daniel Ritchie, Manolis Savva, and Thomas Funkhouser
More informationData Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/004 1
More informationVisual Tracking (1) Tracking of Feature Points and Planar Rigid Objects
Intelligent Control Systems Visual Tracking (1) Tracking of Feature Points and Planar Rigid Objects Shingo Kagami Graduate School of Information Sciences, Tohoku University swk(at)ic.is.tohoku.ac.jp http://www.ic.is.tohoku.ac.jp/ja/swk/
More informationStructure from motion
Structure from motion Structure from motion Given a set of corresponding points in two or more images, compute the camera parameters and the 3D point coordinates?? R 1,t 1 R 2,t 2 R 3,t 3 Camera 1 Camera
More informationArticulated Structure from Motion through Ellipsoid Fitting
Int'l Conf. IP, Comp. Vision, and Pattern Recognition IPCV'15 179 Articulated Structure from Motion through Ellipsoid Fitting Peter Boyi Zhang, and Yeung Sam Hung Department of Electrical and Electronic
More informationData Mining Cluster Analysis: Basic Concepts and Algorithms. Slides From Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Slides From Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining
More information