Unsupervised Representation Learning on ModelNet40 (test)
88.4AccuracyFoldingNet
Evaluation results
| Method | Links | |
|---|---|---|
| FoldingNetEvaluation Source=Reported in original paper2019.06 | 88.4 | |
| l-GAN (CD)Evaluation Source=Current paper re-evaluation, Encoder=Same as PointFlow, Preprocessing=Official code on current dataset2019.06 | 87 | |
| PointFlowEncoder=PointFlow Architecture, Preprocessing=Current paper protocol2019.06 | 86.8 | |
| l-GAN (EMD)Evaluation Source=Current paper re-evaluation, Encoder=Same as PointFlow, Preprocessing=Official code on current dataset2019.06 | 86.7 | |
| MRTNet-VAEEvaluation Source=Reported in original paper2019.06 | 86.4 | |
| PointGrowEvaluation Source=Reported in original paper2019.06 | 85.7 | |
| l-GAN (CD)Evaluation Source=Reported in original paper2019.06 | 84.5 | |
| l-GAN (EMD)Evaluation Source=Reported in original paper2019.06 | 84 | |
| 3D-GANEvaluation Source=Reported in original paper2019.06 | 83.3 | |
| LFDEvaluation Source=Reported in original paper2019.06 | 75.5 | |
| VConv-DAEEvaluation Source=Reported in original paper2019.06 | 75.5 | |
| T-L NetworkEvaluation Source=Reported in original paper2019.06 | 74.4 | |
| SPHEvaluation Source=Reported in original paper2019.06 | 68.2 |