Classifying point clouds at the facade-level using geometric features and deep learning networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913229742014464 |
|---|---|
| author | Tan, Yue Wysocki, Olaf Hoegner, Ludwig Stilla, Uwe |
| author_facet | Tan, Yue Wysocki, Olaf Hoegner, Ludwig Stilla, Uwe |
| contents | 3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level. Our experiments conclude that such early-fused features improve deep learning methods' performance. This method can be applied for compensating deep learning networks' ability in capturing local geometric information and promoting the advancement of semantic segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06506 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Classifying point clouds at the facade-level using geometric features and deep learning networks Tan, Yue Wysocki, Olaf Hoegner, Ludwig Stilla, Uwe Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level. Our experiments conclude that such early-fused features improve deep learning methods' performance. This method can be applied for compensating deep learning networks' ability in capturing local geometric information and promoting the advancement of semantic segmentation. |
| title | Classifying point clouds at the facade-level using geometric features and deep learning networks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.06506 |