3D Point Cloud Classification on ScanObjectNN

Metric: Mean Accuracy (higher is better)

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#	Model↕	Mean Accuracy▼	Extra Data	Paper	Date↕	Code
1	GPSFormer	93.8	No	GPSFormer: A Global Perception and Local Structu...	2024-07-18	Code
2	GPSFormer-elite	92.51	No	GPSFormer: A Global Perception and Local Structu...	2024-07-18	Code
3	ULIP-2 + PointNeXt	91.2	Yes	ULIP-2: Towards Scalable Multimodal Pre-training...	2023-05-14	Code
4	ULIP-2 + PointNeXt (no voting)	90.3	Yes	ULIP-2: Towards Scalable Multimodal Pre-training...	2023-05-14	Code
5	DeLA	89.3	No	Decoupled Local Aggregation for Point Cloud Lear...	2023-08-31	Code
6	PointMLP∗ + JM3D	88.7	Yes	Beyond First Impressions: Integrating Joint Mult...	2023-08-06	Code
7	ULIP + PointNeXt	88.6	Yes	ULIP: Learning a Unified Representation of Langu...	2022-12-10	Code
8	PointConT	88.5	No	Point Cloud Classification Using Content-based T...	2023-03-08	Code
9	ULIP + PointMLP	88.5	Yes	ULIP: Learning a Unified Representation of Langu...	2022-12-10	Code
10	KPConvX-L	88.1	No	KPConvX: Modernizing Kernel Point Convolution wi...	2024-05-21	Code
11	PointNeXt+Local	87.4	No	Local Neighborhood Features for 3D Classification	2022-12-09	Code
12	Ours	87.2	No	-	-	-
13	PointNeXt+HyCoRe	87	No	Rethinking the compositionality of point clouds ...	2022-09-21	Code
14	SPoTr	86.8	No	Self-positioning Point-based Transformer for Poi...	2023-03-29	Code
15	PointNeXt	86.8	No	PointNeXt: Revisiting PointNet++ with Improved T...	2022-06-09	Code
16	PointVector-S	86.2	No	PointVector: A Vector Representation In Point Cl...	2022-05-21	Code
17	PointStack	86.2	No	Advanced Feature Learning on Point Clouds using ...	2022-05-20	Code
18	PointConT (no voting)	86	No	Point Cloud Classification Using Content-based T...	2023-03-08	Code
19	point2vec	86	Yes	Point2Vec for Self-Supervised Representation Lea...	2023-03-29	Code
20	PointCMT	84.8	No	Let Images Give You More:Point Cloud Cross-Modal...	2022-10-09	Code
21	PointMLP	84.4	No	Rethinking Network Design and Local Geometry in ...	2022-02-15	Code
22	PointMLP-elite	81.8	No	Rethinking Network Design and Local Geometry in ...	2022-02-15	Code
23	Point-TnT	81	No	Points to Patches: Enabling the Use of Self-Atte...	2022-04-08	Code
24	PatchAugment	79.7	No	-	-	Code
25	PRA-Net	79.1	No	PRA-Net: Point Relation-Aware Network for 3D Poi...	2021-12-09	Code
26	DRNet	78	No	Dense-Resolution Network for Point Cloud Classif...	2020-05-14	Code
27	GBNet	77.8	No	Geometric Back-projection Network for Point Clou...	2019-11-28	Code
28	PointNet++	75.4	No	PointNet++: Deep Hierarchical Feature Learning o...	2017-06-07	Code
29	PointCNN	75.1	No	PointCNN: Convolution On $\mathcal{X}$-Transform...	2018-01-23	Code
30	DGCNN	73.6	No	Dynamic Graph CNN for Learning on Point Clouds	2018-01-24	Code
31	SpiderCNN	69.8	No	SpiderCNN: Deep Learning on Point Sets with Para...	2018-03-30	Code
32	PointNet	63.4	No	PointNet: Deep Learning on Point Sets for 3D Cla...	2016-12-02	Code

#1GPSFormerSOTA
93.8
Mean Accuracy· 2024-07-18
GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding Code
#2GPSFormer-elite
92.51
Mean Accuracy· 2024-07-18
GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding Code
#3ULIP-2 + PointNeXtSOTA
91.2
Mean Accuracy· Extra Data· 2023-05-14
ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding Code
#4ULIP-2 + PointNeXt (no voting)
90.3
Mean Accuracy· Extra Data· 2023-05-14
ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding Code
#5DeLA
89.3
Mean Accuracy· 2023-08-31
Decoupled Local Aggregation for Point Cloud Learning Code
#6PointMLP∗ + JM3D
88.7
Mean Accuracy· Extra Data· 2023-08-06
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation Code
#7ULIP + PointNeXtSOTA
88.6
Mean Accuracy· Extra Data· 2022-12-10
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding Code
#8PointConT
88.5
Mean Accuracy· 2023-03-08
Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space Code
#9ULIP + PointMLP
88.5
Mean Accuracy· Extra Data· 2022-12-10
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding Code
#10KPConvX-L
88.1
Mean Accuracy· 2024-05-21
KPConvX: Modernizing Kernel Point Convolution with Kernel Attention Code
#11PointNeXt+LocalSOTA
87.4
Mean Accuracy· 2022-12-09
Local Neighborhood Features for 3D Classification Code
#12Ours
87.2
Mean Accuracy
No paper
#13PointNeXt+HyCoReSOTA
87
Mean Accuracy· 2022-09-21
Rethinking the compositionality of point clouds through regularization in the hyperbolic space Code
#14SPoTr
86.8
Mean Accuracy· 2023-03-29
Self-positioning Point-based Transformer for Point Cloud Understanding Code
#15PointNeXtSOTA
86.8
Mean Accuracy· 2022-06-09
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies Code
#16PointVector-S
86.2
Mean Accuracy· 2022-05-21
PointVector: A Vector Representation In Point Cloud Analysis Code
#17PointStackSOTA
86.2
Mean Accuracy· 2022-05-20
Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling Code
#18PointConT (no voting)
86
Mean Accuracy· 2023-03-08
Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space Code
#19point2vec
86
Mean Accuracy· Extra Data· 2023-03-29
Point2Vec for Self-Supervised Representation Learning on Point Clouds Code
#20PointCMT
84.8
Mean Accuracy· 2022-10-09
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis Code
#21PointMLPSOTA
84.4
Mean Accuracy· 2022-02-15
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework Code
#22PointMLP-elite
81.8
Mean Accuracy· 2022-02-15
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework Code
#23Point-TnT
81
Mean Accuracy· 2022-04-08
Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition Code
#24PatchAugment
79.7
Mean Accuracy
No paperCode
#25PRA-NetSOTA
79.1
Mean Accuracy· 2021-12-09
PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis Code
#26DRNetSOTA
78
Mean Accuracy· 2020-05-14
Dense-Resolution Network for Point Cloud Classification and Segmentation Code
#27GBNetSOTA
77.8
Mean Accuracy· 2019-11-28
Geometric Back-projection Network for Point Cloud Classification Code
#28PointNet++SOTA
75.4
Mean Accuracy· 2017-06-07
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space Code
#29PointCNN
75.1
Mean Accuracy· 2018-01-23
PointCNN: Convolution On $\mathcal{X}$-Transformed Points Code
#30DGCNN
73.6
Mean Accuracy· 2018-01-24
Dynamic Graph CNN for Learning on Point Clouds Code
#31SpiderCNN
69.8
Mean Accuracy· 2018-03-30
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters Code
#32PointNetSOTA
63.4
Mean Accuracy· 2016-12-02
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Code