A Riemannian Framework for Matching Point Clouds Represented by the Schrödinger Distance Transform

In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrödinger distance transform (SDT) representation. This is achieved by solving a static Schrödinger equation instead of the...

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Bibliographic Details
Published in:2014 IEEE Conference on Computer Vision and Pattern Recognition Vol. 2014; pp. 3756 - 3761
Main Authors: Yan Deng, Rangarajan, Anand, Eisenschenk, Stephan, Vemuri, Baba C.
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.06.2014
Subjects:
Algorithms
Data models
Density
Equations
Exact solutions
Matching
Mathematical analysis
Measurement
non-rigid registration
Optimization
Point clouds matching
Representations
Riemannian Manifold
Schrodinger Distance Transform
Schroedinger equation
Shape
Three dimensional models
Three-dimensional displays
Transformations
Transforms
Unit Hilbert Sphere
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrödinger distance transform (SDT) representation. This is achieved by solving a static Schrödinger equation instead of the corresponding static Hamilton-Jacobi equation in this setting. The SDT representation is an analytic expression and following the theoretical physics literature, can be normalized to have unit 2 norm - making it a square-root density, which is identified with a point on a unit Hilbert sphere, whose intrinsic geometry is fully known. The Fisher-Rao metric, a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere. In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm. We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques. Finally, to evaluate the performance of our algorithm - dubbed SDTM - we present several synthetic and real data examples along with extensive comparisons to state-of-the-art techniques. The experiments show that our algorithm outperforms state-of the-art point set registration algorithms on many quantitative metrics.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2014.486