Results for 3DASPP-SCE
Submission data
| Full name | 3DASPP for Semantic Label and Semantic Context Embedding. |
| Description | Firstly, we used a 3D ASPP module for the semantic segmentation of 3d point cloud.
Secondly, we embedded the semantic context feature to global scene classification, thus can enhance the semantic correlation explicitly. In addition, we used softmax to enhance the learning for semantic context feature during training stage. |
| Input Data Types | Uses Geometry Uses 3D |
| Programming language(s) | Python with CUDA |
| Hardware | Nvidia V100 |
| Submission creation date | 17 Jun, 2021 |
| Last edited | 17 Jun, 2021 |
Scene type classification results
| Info | avg iou | apartment | bathroom | bedroom / hotel | bookstore / library | conference room | copy/mail room | hallway | kitchen | laundry room | living room / lounge | misc | office | storage / basement / garage |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.556 | 0.500 | 0.938 | 0.778 | 0.667 | 1.000 | 0.250 | 0.500 | 0.750 | 0.333 | 0.500 | 0.000 | 0.812 | 0.200 |