3D Shape Classification on ModelNet 10 1.0 (test)
97.1AccuracyVRN Ensemble
Evaluation results
| Method | Links | ||
|---|---|---|---|
| VRN EnsembleInput configuration=additional input or more points2019.04 | 97.1 | — | |
| SO-NetInput configuration=additional input or more points2019.04 | 95.7 | 95.5 | |
| A-CNNInput configuration=1024 points2019.04 | 95.5 | 95.3 | |
| Point2SequenceInput configuration=1024 points2019.04 | 95.3 | 95.1 | |
| Kd-NetInput configuration=1024 points, depth=152019.04 | 94 | 93.5 | |
| 3D ShapeNetsfeatures=5th layer, classifier=linear SVM2014.06 | 83.54 | — | |
| LFDdimensions=4,700, classifier=linear SVM2014.06 | 79.87 | — | |
| SPHdimensions=544, classifier=linear SVM2014.06 | 79.79 | — | |
| KCNetInput configuration=1024 points2019.04 | — | 94.4 | |
| PCNNInput configuration=1024 points2019.04 | — | 94.9 |