Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
Spectral surface reconstruction from noisy point clouds
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generic software design for Delaunay refinement meshing
Computational Geometry: Theory and Applications
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Bandwidth Selection and Reconstruction Quality in Point-Based Surfaces
IEEE Transactions on Visualization and Computer Graphics
Provable surface reconstruction from noisy samples
Computational Geometry: Theory and Applications
Boundary fitting for 2D curve reconstruction
The Visual Computer: International Journal of Computer Graphics
ℓ1-Sparse reconstruction of sharp point set surfaces
ACM Transactions on Graphics (TOG)
Cone carving for surface reconstruction
ACM SIGGRAPH Asia 2010 papers
Geometric Inference for Probability Measures
Foundations of Computational Mathematics
Computer Graphics Forum
Growing Least Squares for the Analysis of Manifolds in Scale-Space
Computer Graphics Forum
The domain of a point set surface
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Modeling Kinect Sensor Noise for Improved 3D Reconstruction and Tracking
3DIMPVT '12 Proceedings of the 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission
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We propose a noise-adaptive shape reconstruction method specialized to smooth, closed shapes. Our algorithm takes as input a defect-laden point set with variable noise and outliers, and comprises three main steps. First, we compute a novel noise-adaptive distance function to the inferred shape, which relies on the assumption that the inferred shape is a smooth submanifold of known dimension. Second, we estimate the sign and confidence of the function at a set of seed points, through minimizing a quadratic energy expressed on the edges of a uniform random graph. Third, we compute a signed implicit function through a random walker approach with soft constraints chosen as the most confident seed points computed in previous step.