Scale selection for classification of point-sampled 3D surfaces

By Jean-François Lalonde, Ranjith Unnikrishnan, N. Vandapel, Martial Hebert. 2005

97 citations recorded by the source corpus.

Three-dimensional ladar data are commonly used to perform scene understanding for outdoor mobile robots, specifically in natural terrain. One effective method is to classify points using features based on local point cloud distribution into surfaces, linear structures or clutter volumes. But the local features are computed using 3D points within a support-volume. Local and global point density variations and the presence of multiple manifolds make the problem of selecting the size of this support volume, or scale, challenging. In this paper, we adopt an approach inspired by recent developments in computational geometry (Mitra et al., 2005) and investigate the problem of automatic data-driven scale selection to improve point cloud classification. The approach is validated with results using data from different sensors in various environments classified into different terrain types (vegetation, solid surface and linear structure).

Subfields: Computational Mechanics, Geology

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Citations in the corpus

DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS (2012, background): “Such works have been theoretically improved by Lalonde et al. (2005), no more requiring knowledge on the data distribution, and applied to mobile mapping datasets.”
Shape-based recognition of 3D point clouds in urban environments (2009, methodology): “In [15], points are labeled with a Bayesian classifier based on local properties.”
Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) (2013, methodology): “This highlights the intuitive notion that the scale at which the normal should be estimated depends on the surface roughness (Mitra and Nguyen, 2003; Lalonde et al., 2005; Bae et al., 2009).”
Aligning point cloud views using persistent feature histograms (2008, methodology): “scale) for normal estimation on 3D point cloud data [10], [11] as well as principal curvatures [12], [13], [14] on multiple scales.”
A Closed-Form Expression of the Positional Uncertainty for 3D Point Clouds (2009, methodology): “Mitra et al. [24] proposed a method of finding a radius for each neighborhood given a threshold value, with an improvement to the method discussed by Lalonde et al. [ 21 ].”
Persistent Point Feature Histograms for 3D Point Clouds (2008, methodology): “scale) for normal estimation on 3D point cloud data [8, 9 ] as well as principal curvatures [10,11,12] on multiple scales.”