Name
Title | ||
|---|---|---|
Local Fusion Attention Network for Semantic Segmentation of Building Facade Point Clouds |
Abstract | ||
|---|---|---|
Automatic building facade point cloud semantic segmentation is an important step in 3-D urban building reconstruction. How to correctly segment the components (e.g., windows, walls, and columns) from the building facade is still a challenging task. According to the characteristics of building facade point clouds, we introduce local fusion attention network (LFA-Net), an efficient neural network that learns LFA features from building facade point clouds, for better capturing the local neighborhood structure information of each point. The core of LFA-Net is the LFA module, which consists of three neural units: local graph attention (LGA), local aggregation attention (LAA), and fusion attention (FA). The LFA-Net is the standard encoder-decoder architecture. Experiments demonstrate that our LFA-Net outperforms the state-of-the-art methods on the large-scale building facade point cloud dataset. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3126735 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Buildings, Three-dimensional displays, Semantics, Image segmentation, Aggregates, Task analysis, Neural networks, Building facade, fusion attention (FA), local aggregation attention (LAA), local graph attention (LGA), semantic segmentation | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanfei Su | 1 | 0 | 0.68 |
| Weiquan Liu | 2 | 0 | 0.68 |
| Ming Cheng | 3 | 54 | 13.93 |
| Zhimin Yuan | 4 | 0 | 1.01 |
| Cheng Wang | 5 | 118 | 29.56 |