Name
Title | ||
|---|---|---|
DLA-Net: Learning dual local attention features for semantic segmentation of large-scale building facade point clouds |
Abstract | ||
|---|---|---|
•The first large-scale fine-grained building facade 3D point clouds dataset benchmark for semantic segmentation, the dataset has some proprietary characteristics.•An enhanced position encoding block, which aggregates different spatial information to learn more local geometric structure information.•The Dual Local Attention (DLA) module consists of two blocks including the self-attention block and the attentive pooling block.•The proposed DLA-Net presents decent performance under the building facade dataset. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2021.108372 | Pattern Recognition |
Keywords | DocType | Volume |
Semantic segmentation,Building facade,Self-attention,Attentive pooling,DLA-Net | Journal | 123 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanfei Su | 1 | 0 | 0.34 |
| Weiquan Liu | 2 | 11 | 6.25 |
| Zhimin Yuan | 3 | 0 | 0.34 |
| Ming Cheng | 4 | 54 | 13.93 |
| Zhihong Zhang | 5 | 0 | 0.34 |
| Xuelun Shen | 6 | 0 | 1.35 |
| Cheng Wang | 7 | 118 | 29.56 |