We use Intersection over Union (IoU) and Overall Accuracy (OA) as metrics. For more details hover the curser over the symbols or click on a classifier. In order to sort the results differently click on a symbol.
| Name | A_IoU | OA | [s] | IoU 1 | IoU 2 | IoU 3 | IoU 4 | IoU 5 | IoU 6 | IoU 7 | IoU 8 | |
| 1 | DLUT_SR | 0.563 | 0.860 | 1.00 | 0.953 | 0.849 | 0.548 | 0.296 | 0.832 | 0.192 | 0.320 | 0.518 |
| Anonymous submission | ||||||||||||
| 2 | TMLC-MSR | 0.542 | 0.862 | 1800.00 | 0.898 | 0.745 | 0.537 | 0.268 | 0.888 | 0.189 | 0.364 | 0.447 |
| Timo Hackel, Jan D. Wegner, Konrad Schindler: Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals - ISPRS Congress, Prague, 2016 | ||||||||||||
| 3 | DeePr3SS | 0.585 | 0.889 | 0.00 | 0.856 | 0.832 | 0.742 | 0.324 | 0.897 | 0.185 | 0.251 | 0.592 |
| F. Lawin, M. Danelljan, P. Tosteberg, G. Bhat, F. Khan, M. Felsberg. Deep Projective 3D Semantic Segmentation. In , 2017. | ||||||||||||
| 4 | SnapNet_ | 0.591 | 0.886 | 3600.00 | 0.820 | 0.773 | 0.797 | 0.229 | 0.911 | 0.184 | 0.373 | 0.644 |
| Unstructured point cloud semantic labeling using deep segmentation networks. A. Boulch, B. Le Saux and N. Audebert, Eurographics 3DOR 2017 | ||||||||||||
| 5 | DeepNet | 0.437 | 0.772 | 64800.00 | 0.838 | 0.385 | 0.548 | 0.085 | 0.841 | 0.151 | 0.223 | 0.423 |
| Anonymous submission | ||||||||||||
| 6 | TML-PCR | 0.384 | 0.740 | 0.00 | 0.726 | 0.730 | 0.485 | 0.224 | 0.707 | 0.050 | 0.000 | 0.150 |
| Mind the gap: modeling local and global context in (road) networks: Javier Montoya, Jan D. Wegner, Lubor Ladicky, Konrad Schindler. In: German Conference on Pattern Recognition (GCPR), Münster, Germany, 2014 | ||||||||||||