Results for 3DMV, FTSDF
Submission data
Full name | 3DMV with Deeplab and FTSDF |
Description | Reimplementation of "3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation, Angela Dai, Matthias Nießner" with some modifications:
1. We use Deeplab instead of ENet 2. We only fine-tune Deeplab on the 2D labels without end-to-end training 3. As geometry channel we only use the flipped TSDF described in Semantic Scene Completion from a Single Depth Image Shuran Song Fisher Yu Andy Zeng Angel X. Chang Manolis Savva Thomas Funkhouser |
Publication authors | Martin Bokeloh, Juergen Sturm |
Input Data Types | Uses Color,Uses Geometry Uses 2D,Uses 3D |
Programming language(s) | C++/Python/Tensorflow |
Hardware | V100 |
Submission creation date | 2 Oct, 2018 |
Last edited | 2 Oct, 2018 |
3D semantic label results
Info | avg iou | bathtub | bed | bookshelf | cabinet | chair | counter | curtain | desk | door | floor | otherfurniture | picture | refrigerator | shower curtain | sink | sofa | table | toilet | wall | window |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.501 | 0.558 | 0.608 | 0.424 | 0.478 | 0.690 | 0.246 | 0.586 | 0.468 | 0.450 | 0.911 | 0.394 | 0.160 | 0.438 | 0.212 | 0.432 | 0.541 | 0.475 | 0.742 | 0.727 | 0.477 |