Robust point cloud segmentation and normal estimation
-
A new and more robust multivariate statistical method for point cloud normal and local curvature estimation
-
This method results in a more reliable estimation of normals in noisy 3D point clouds containing sharp features and planar transitions
-
A new region growing method for context-free segmentation of unstructured noisy point clouds with outliers
-
The segmentation method uses a locally adaptive spatial connectivity analysis to account geometric features during segment growth
-
Qualitative and quantitative evaluation of the presented method using a series of challenging point clouds
PCA - based Normal Estimation
Our Robust Normal Estimation Approach
Related Publications
-
Khaloo, A., and Lattanzi, D. (2017) "Robust Normal Estimation and Region Growing Segmentation of Infrastructure 3D Point Clouds," Advanced Engineering Informatics, 34, 1-16.
-
Khaloo, A., and Lattanzi, D. (2017) "Robust Outlier Detection and Normal Estimation in Noisy Infrastructure 3D Point Clouds," ASCE International Workshop on Computing in Civil Engineering (IWCCE), Seattle, WA, June 2017. (travel award)
