One of the big challenges in 3D LIDAR point-cloud segmentation is detailed ground extraction, especially in high vegetated area. In some applications, it requires to extract the ground points from the LIDAR data such that the details are preserved as much as possible, however, most of the time the details and the noise are coupled and it is difficult to remove the noise whereas the ground details are preserved. Imagine the case where you have the LIDAR point cloud over a creek covered by multilayer canopies including ground flora and you would like to extract the creek from the data set by preserving the ground details as much as you can. This would be a very labor-intensive task for human, so a better choice might be to develop an automatic process for computer to complete the task for us. Even for a computer, this can be a very labor-intensive task due to the number of points in the area is extremely high.
In 2004, I and my former adviser, Dr. Kenneth C. Slatton, developed a multiscale information-theoretic based algorithm for ground segmentation. The method works well in real-world applications and is used in several publications. The MATLAB toolbox is available here. The brief manual can be found here.
I would like to thank my colleagues at National Center for Airborne Laser Mapping (NCALM), Adaptive Signal Processing Laboratory (ASPL) and Geosensing group at University of Florida who use the algorithm on their work and give tons of useful suggestions to improve this algorithm up until now; Dr. Jhon Caceres for very nice GUI; Dr. Sowmya Selvarajan for the first-ever manual for this toolbox. Last but not least, I would like to thank Dr. Kenneth Clint Slatton for wonderful ideas and guidance–we still have an unpublished journal to fulfill .
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 K. Kampa and K. Clint Slatton, “Information-Theoretic Hierarchical Segmentation of Airborne Laser Swath Mapping Data,” IEEE Transactions in Geoscience and Remote Sensing, (in preparation).
 K. Kampa and K. C. Slatton, “An Adaptive Multiscale Filter for Segmenting Vegetation in ALSM Data,” Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), vol. 6, Sep. 2004, pp. 3837 – 3840.
A brief slides can be found here.
I found some interesting papers using over-segmented data (voxel) concept and CRF with terrestrial LIDAR point cloud.
Volumetric Visualization of Multiple-return Lidar Data: Using Voxels by Jason Stoker [link]
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data by D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, and A. Ng. (CVPR2005)
Conditional Random Field for 3D point clouds with Adaptive Data Reduction by E. H. Lim and D. Suter (CW2007)
Multi-scale Conditional Random Fields for Over-segmented Irregular 3D Point Clouds Classification by E. H. Lim and D. Suter (CVPR2008)
3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields by E. H. Lim and D. Suter (CVPR2008)
Shape-based Recognition of 3D Point Clouds in Urban Environments by Aleksey Golovinskiy et al (ICCV2009)