Technical Section: Robust normal estimation for point clouds with sharp features
Computers and Graphics
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data
Pattern Recognition Letters
Robust classification of curvilinear and surface-like structures in 3d point cloud data
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
3D geometry from uncalibrated images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
3D Geometric Scale Variability in Range Images: Features and Descriptors
International Journal of Computer Vision
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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 3-D 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 [5] 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)鹿.