The Strength of Weak Learnability
Machine Learning
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Automatic reconstruction of surfaces and scalar fields from 3D scans
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Fitting smooth surfaces to dense polygon meshes
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
A new Voronoi-based surface reconstruction algorithm
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Spherical averages and applications to spherical splines and interpolation
ACM Transactions on Graphics (TOG)
Multi-level partition of unity implicits
ACM SIGGRAPH 2003 Papers
Shape modeling with point-sampled geometry
ACM SIGGRAPH 2003 Papers
A Simple Approach for Point-Based Object Capturing and Rendering
IEEE Computer Graphics and Applications
Normal Improvement for Point Rendering
IEEE Computer Graphics and Applications
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Spectral surface reconstruction from noisy point clouds
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Bilateral estimation of vertex normal for point-sampled models
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
The power crust, unions of balls, and the medial axis transform
Computational Geometry: Theory and Applications
Post-processing of scanned 3D surface data
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Normal estimation for point clouds: a comparison study for a Voronoi based method
SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics
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The majority of the existing techniques for surface reconstruction and the closely related problem of normal estimation are deterministic. Their main advantages are the speed and, given a reasonably good initial input, the high quality of the reconstructed surfaces. Nevertheless, their deterministic nature may hinder them from effectively handling incomplete data with noise and outliers. In our previous work [1], we applied a statistical technique, called ensembles, to the problem of surface reconstruction. We showed that an ensemble can improve the performance of a deterministic algorithm by putting it into a statistics based probabilistic setting. In this paper, with several experiments, we further study the suitability of ensembles in surface reconstruction, and also apply ensembles to normal estimation. We experimented with a widely used normal estimation technique [2] and Multi-level Partitions of Unity implicits for surface reconstruction [3], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.