3D Classification on ModelNet40

Metric: Classification Accuracy (higher is better)

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#	Model↕	Classification Accuracy▼	Extra Data	Paper	Date↕	Code
1	Ours	93.6	No	Exploiting Inductive Bias in Transformer for Poi...	2023-04-27	Code
2	G3DNet-18 MLP, Fine-Tuned, Vote	91.7	Yes	-	-	Code
3	CrossMoCo	91.49	No	-	-	Code
4	O-CNN(6)	89.9	No	O-CNN: Octree-based Convolutional Neural Network...	2017-12-05	Code
5	3D-PointCapsNet	89.3	No	3D Point Capsule Networks	2018-12-27	Code
6	Spherical Kernel	89.3	No	Spherical Kernel for Efficient Graph Convolution...	2019-09-20	Code
7	ECC (12 votes)	83.2	No	Dynamic Edge-Conditioned Filters in Convolutiona...	2017-04-10	Code

#1OursSOTA
93.6
Classification Accuracy· 2023-04-27
Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation Code
#2G3DNet-18 MLP, Fine-Tuned, Vote
91.7
Classification Accuracy· Extra Data
No paperCode
#3CrossMoCo
91.49
Classification Accuracy
No paperCode
#4O-CNN(6)SOTA
89.9
Classification Accuracy· 2017-12-05
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis Code
#53D-PointCapsNet
89.3
Classification Accuracy· 2018-12-27
3D Point Capsule Networks Code
#6Spherical Kernel
89.3
Classification Accuracy· 2019-09-20
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds Code
#7ECC (12 votes)SOTA
83.2
Classification Accuracy· 2017-04-10
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Code