3D Object Recognition On Modelnet40
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| R2-MLP-36 | 97.7% | R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition | |
| MVCNN-MultiRes | 93.8% | Volumetric and Multi-View CNNs for Object Classification on 3D Data | |
| FPNN (4-FCs + NF) | 88.4% | FPNN: Field Probing Neural Networks for 3D Data | |
| Variational Shape Learner | 84.5% | Learning a Hierarchical Latent-Variable Model of 3D Shapes | |
| MeshWalker (ours) | 92.3% | MeshWalker: Deep Mesh Understanding by Random Walks | |
| MVT-small | 97.5% | MVT: Multi-view Vision Transformer for 3D Object Recognition |
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