3D Object Classification On Modelnet40
Metrics
Classification Accuracy
Results
Performance results of various models on this benchmark
Model Name | Classification Accuracy | Paper Title | Repository |
|---|---|---|---|
| G3DNet-18 MLP, Fine-Tuned, Vote | 91.7 | General-Purpose Deep Point Cloud Feature Extractor | |
| CrossMoCo | 91.49 | CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud | |
| ECC (12 votes) | 83.2 | Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs | - |
| O-CNN(6) | 89.9 | O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis | - |
| Ours | 93.6 | Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation | - |
| Spherical Kernel | 89.3 | Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds | - |
| 3D-PointCapsNet | 89.3 | 3D Point Capsule Networks | - |
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