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SOTA
Supervised Only 3D Point Cloud Classification
Supervised Only 3D Point Cloud Classification
Supervised Only 3D Point Cloud Classification
평가 지표
Number of params (M)
Overall Accuracy (PB_T50_RS)
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Number of params (M)
Overall Accuracy (PB_T50_RS)
Paper Title
Repository
Point-PN
0.8
87.1
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
PointNet
3.5
68.0
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Mamba3D
16.9
92.64
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
PCM
34.2
88.1
Point Cloud Mamba: Point Cloud Learning via State Space Model
DeLA
5.3
90.4
Decoupled Local Aggregation for Point Cloud Learning
SPoTr
1.7
88.6
Self-positioning Point-based Transformer for Point Cloud Understanding
DGCNN
1.8
78.1
Dynamic Graph CNN for Learning on Point Clouds
Transformer
22.1
77.24
Attention Is All You Need
PointMLP
12.6
85.4
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
PointNet++
1.5
77.9
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Mamba3D (no voting)
16.9
91.81
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
PointNeXt
1.4
87.8
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
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