Abstract
The classification of 3d point cloud data is an important component of applications such as map generation and architectural modeling. However, the complexity of the scenes together with the level of noise in the data acquired through mobile laser range-scanning make this task quite difficult. We propose a novel classification method that relies on a combination of edge, node, and relative density information within an Associative Markov Network framework. The main application of our work is the classification of the structures within a point cloud into curvilinear, surface-like, and noise components. We are able to robustly extract complicated structures such as tree branches. The measures taken to ensure the robustness of our method generalize and can be leveraged in noise reduction applications as well. We compare our work with another state of the art classification technique, namely Directional Associative Markov Network, and show that our method can achieve significantly higher accuracy in the classification of the 3d point clouds.
| Original language | English (US) |
|---|---|
| Title of host publication | Advances in Visual Computing - 7th International Symposium, ISVC 2011, Proceedings |
| Pages | 699-708 |
| Number of pages | 10 |
| Edition | PART 1 |
| DOIs | |
| State | Published - Oct 5 2011 |
| Event | 7th International Symposium on Visual Computing, ISVC 2011 - Las Vegas, NV, United States Duration: Sep 26 2011 → Sep 28 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 1 |
| Volume | 6938 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 7th International Symposium on Visual Computing, ISVC 2011 |
|---|---|
| Country | United States |
| City | Las Vegas, NV |
| Period | 9/26/11 → 9/28/11 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Robust classification of curvilinear and surface-like structures in 3d point cloud data. / Kamali, Mahsa; Stroila, Matei; Cho, Jason; Shaffer, Eric Gene; Hart, John C.
Advances in Visual Computing - 7th International Symposium, ISVC 2011, Proceedings. PART 1. ed. 2011. p. 699-708 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6938 LNCS, No. PART 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Robust classification of curvilinear and surface-like structures in 3d point cloud data
AU - Kamali, Mahsa
AU - Stroila, Matei
AU - Cho, Jason
AU - Shaffer, Eric Gene
AU - Hart, John C
PY - 2011/10/5
Y1 - 2011/10/5
N2 - The classification of 3d point cloud data is an important component of applications such as map generation and architectural modeling. However, the complexity of the scenes together with the level of noise in the data acquired through mobile laser range-scanning make this task quite difficult. We propose a novel classification method that relies on a combination of edge, node, and relative density information within an Associative Markov Network framework. The main application of our work is the classification of the structures within a point cloud into curvilinear, surface-like, and noise components. We are able to robustly extract complicated structures such as tree branches. The measures taken to ensure the robustness of our method generalize and can be leveraged in noise reduction applications as well. We compare our work with another state of the art classification technique, namely Directional Associative Markov Network, and show that our method can achieve significantly higher accuracy in the classification of the 3d point clouds.
AB - The classification of 3d point cloud data is an important component of applications such as map generation and architectural modeling. However, the complexity of the scenes together with the level of noise in the data acquired through mobile laser range-scanning make this task quite difficult. We propose a novel classification method that relies on a combination of edge, node, and relative density information within an Associative Markov Network framework. The main application of our work is the classification of the structures within a point cloud into curvilinear, surface-like, and noise components. We are able to robustly extract complicated structures such as tree branches. The measures taken to ensure the robustness of our method generalize and can be leveraged in noise reduction applications as well. We compare our work with another state of the art classification technique, namely Directional Associative Markov Network, and show that our method can achieve significantly higher accuracy in the classification of the 3d point clouds.
UR - http://www.scopus.com/inward/record.url?scp=80053353763&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053353763&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24028-7_64
DO - 10.1007/978-3-642-24028-7_64
M3 - Conference contribution
AN - SCOPUS:80053353763
SN - 9783642240270
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 699
EP - 708
BT - Advances in Visual Computing - 7th International Symposium, ISVC 2011, Proceedings
ER -