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Scale-Space Representation for Matching of 3D Models
Dmitriy Bespalov Department of Computer Science College of Engineering Drexel University 3141 Chestnut Street Philadelphia, PA 19104
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Overview of the Thesis Introduction Approach Experimental results
Overview of feature decomposition Variations of feature decomposition Matching Experimental results Conclusions, contributions and future work
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Motivation & Related Work
? query Database query result ? query
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Goals of this Work Feature extraction technique
Models in polyhedral representation Using local information Tolerance to noise
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CAD vs Shape Representation
CAD Representation Shape Representation conversion is hard Topologically and geometrically consistent Implicit surfaces Analytic surfaces NURBS, etc Approximate representation, error prone Mesh Point cloud Can produce with laser scanners conversion is easy
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What is a Scale Space Representation?
Commonly used for Coarse-to-Fine representations of an object Very popular in computer Vision Constructed via spatial filters: Gaussian pyramids, Wavelets… Basic Idea: At each scale, topologically relevant components will decompose the object into so called salient parts Recursive application of this paradigm will create the object’s scale space hierarchy
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Algorithm Overview (I)
Obtain mesh representation M 2. Define measurement function: assign distance measure to every pair of points or triangles
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Algorithm Overview (II)
3. Decompose M into relevant components using a singular value decomposition of distance matrix D Note: this creates a clustering based on the angle between a vector Oti and the basis vectors (ck, ck-1)
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Algorithm Overview (III)
Recursive feature decomposition using two principle components creates binary feature trees. Use leaf nodes as features.
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Algorithmic Complexity
Bisection process: SVD decomposition takes O(n3). Polyhedral representation creates a 3D lattice; if only neighboring vertices are used in construction of the distance matrix, SVD decomposition is faster and takes O(n2).
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Variations of Feature Decomposition (I)
Use various distance measures to tune nature of extracted features Global Distance Function Geodesic Distance Function Angular Shortest Path Max-Angle on Angular Shortest Path Global Feature Extraction Local Distance Function Local Feature Extraction
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Controlling Feature Decomposition
Which “feature” is better? Need to control decomposition process to get these features Otherwise, get these features
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Controlling Feature Decomposition: When to Stop?
Depends on the distance measure used At each step of decomposition: decide whether to stop Assign “quality” measure to each bisection
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Controlling Global Feature Decomposition
f measures the “quality” of a bisection Assume decomposition of M1 into M2 and M3 Bisect M1 into M2 and M3 if f(M1) < 0.5 M2 M2 M3 M3
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Controlling Local Feature Decomposition
“Quality” of bisection is angle based Assume decomposition of M1 into M2 and M3 Bisect M1 into M2 and M3 if angular distance between components M2 and M3 is large angle across the border of the cluster is max on the path between most of the pairs of faces in M2 and M3. M2 M3
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Matching for Feature Extraction
features extract features set of features set of features How to match extracted features?
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Matching for Global Feature Extraction
Decomposition trees are near-to-balanced Compare decomposition trees (bottom up dynamic programming) using sub-tree edit distances Calculate model similarity based on an overall similarity of matched components
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Matching for Local Feature Extraction
Decomposition trees can not be used Compare feature graphs (leaves of decomposition trees) Sub-graph isomorphism is used to asses similarity Hill-climbing algorithm with random restarts
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Retrieval Experiments
k-nearest neighbor classification (kNN) Recall and precision measures Precision against recall graphs Relevant models: number of models that fall in the same category as query model Retrieved models: number of models returned by a query Retrieved and Relevant models: number of models returned and that fell into the same category as query model
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Retrieval Using Functional Classification
Techniques used: Reeb graph comparison (Reeb) Global Feature Extraction (Scale-Space) Local Feature Extraction (Local Scale-Space)
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Functional Classification
Springs Screws Gears Nuts Brackets Housings Linkage arms Functional Classification
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Experimental Results for Local Feature Extraction
Feature decomposition on CAD data Feature decomposition on scanned data Retrieval experiments on scanned data
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Experimental Results: CAD Data
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Experimental Results: Scanned Data
From Exact Representation 360° Scan Single Scan
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Experimental Results: Scanned Data
From Exact Representation 360° Scan Single Scan An example of one-to-many correspondence
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Experimental Results: Scanned Data
From Exact Representation 360° Scan Single Scan An example of one-to-one correspondence
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Experimental Results: Scanned Data
From Exact Representation 360° Scan Single Scan An example of one-to-one correspondence
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Experimental Results: Scanned Data
From Exact Representation 360° Scan Single Scan An example of many-to-many correspondence
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Experimental Results: Scanned Data
From Exact Representation 360° Scan Single Scan An example of one-to-many correspondence
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Experimental Results: Scanned Data
From Exact Representation 360° Scan Single Scan An example of one-to-one correspondence
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Retrieval on Partial and Scanned Data
Partial Data From Exact Representation 360° Scan Single Scan query CAD Database
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Retrieval on Partial and Scanned Data
Full Scan Partial Scan Partial CAD
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Retrieval on Partial and Scanned Data
Full Scan Partial Scan Partial CAD
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Retrieval on Partial and Scanned Data
Full Scan Partial Scan Partial CAD
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Summary of Experimental Results
Feature extraction is acceptable Matching could be improved Matching for Global Feature Extraction: Comparison of feature pairs is weak, drawn from Reeb Graph technique Matching for Local Feature Extraction: No feature pairs comparison No many-to-many matching No handling for noise features
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Conclusions & Contributions
Parameterizable feature extraction for CAD Features depend only on distance measure Applicable to partial and scanned data Query CAD database with scanned data Attempted to address matching problem
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Future Work Introduce matching for feature graphs
Better comparison for feature pairs Handle many-to-many matching Identify noise features Develop various distance measures That resemble traditional CAD features Approximate B-Rep from polyhedral models
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Q&A Sponsored by:
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Side Note: Compare Features
This Technique CAD/CAM
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The Eckart-Young Theorem
The Eckart-Young Theorem: Given an n by m matrix X of rank r ≤ m ≤ n, and its singular value decomposition, ULV', where U is an n by m matrix, L is an m by m diagonal matrix of singular values, and V is an m by m matrix such that U'U = In and V'V = VV' = Im with the singular values arranged in decreasing sequence λ1 ≥ λ2 ≥ λ3 ≥ ... ≥ λm ≥ 0 then there exists an n by m matrix B of rank s, s ≤ r, which minimizes the sum of the squared error between the elements of X and the corresponding elements of B when B = UΛsV' where the diagonal elements of the m by m diagonal matrix Λs are λ1 ≥ λ2 ≥ λ3 ≥ ... ≥ λs > λs+1 = λs+2 = ... = λm = 0
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