Feature Extraction From Point Clouds

By Stefan Gumhold, Xinlong Wang, R. Macleod. 2001

373 citations recorded by the source corpus.

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Citations in the corpus

DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS (2012, background): “Consequently, in our context, a more suitable solution is to directly compute shape features (Gumhold et al., 2001; Belton and Lichti, 2006), i.e., low-level primitives that may capture the variability of natural environments.”
Scale selection for classification of point-sampled 3D surfaces (2005, methodology): “Work in [1] uses k-neighborhoods to compensate for differences in sampling rate before computing eigenvalue-based features for detecting surfaces, creases and borders.”
State of the Art in Surface Reconstruction from Point Clouds (2014, unknown): “However, the locality of the feature detection can generate fragmented sharp edges, much like general feature detection approaches [GWM01,PP09].”
Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning (2014, methodology): “[14] developed an edge line extraction method using the local neighbor graph theory for the purpose of surface reconstruction.”
Ridge-valley lines on meshes via implicit surface fitting (2004, methodology): “In geometric modeling, there has been considerable effort to develop robust methods for crease detection on dense triangle meshes [Watanabe and Belyaev 2001; Hubeli and Gross 2001; Page et al. 2002; Stylianou and Farin 2003] and point-sampled surfaces [Gumhold et al. 2001; Pauly et al. 2003].”
Multi‐scale Feature Extraction on Point‐Sampled Surfaces (2003, methodology): “Note that our method for pruning and closing cycles does not require an expensive breadth first search as was done, for example, in [5].”
Nonasymptotic rates for manifold, tangent space and curvature estimation (2017, background): “Several algorithms have been proposed in that case [24, 6, 21, 17], but with no analysis of their performances from a statistical point of view.”
Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification (2014, background): “In geometry, the eigenvalues relate with an ellipsoid that represents the local geometric structure of a point set (Gumhold et al., 2001).”
Estimating surface normals in noisy point cloud data (2003, methodology): “To estimate the maximum local curvature • , we use the method proposed by Gumhold et al. 12 .”