| Title |
Validation of point cloud data for 3D plane detection / |
| Authors |
Lipnickas, A ; Rimkus, K ; Sinkevičius, S |
| DOI |
10.1007/978-3-319-06883-1 |
| ISBN |
9783319068824 |
| eISBN |
9783319068831 |
| Full Text |
|
| Is Part of |
Issues and challenges in artificial intelligence / Z.S. Hippe, J.L. Kulikowski, T. Mroczek, J. Wtorek (eds).. Cham : Springer, 2014. p. 27-37.. ISBN 9783319068824. eISBN 9783319068831 |
| Keywords [eng] |
3D point cloud ; RANdom SAmple Consensus (RANSAC) method ; cluster validity |
| Abstract [eng] |
There are number of plane detection techniques for a given 3D point cloud utilized in different applications. All of the methods measure planes quality by computing sum of square error for a fitted plane model but no one of techniques may count the number of planes in the point cloud. In this chapter we present new strategy for validating number of found planes in the 3D:point cloud by applied cluster validity indices. For a planes finding in point cloud we have engaged the RANdom SAmple Consensus (RANSAC) method to synthetic and real scanned data. The experimental results have shown that the cluster validity indices may help in tuning RANSAC parameters as well as in determination the number of planes in 3D data. |
| Published |
Cham : Springer, 2014 |
| Type |
Book part |
| Language |
English |
| Publication date |
2014 |
