Object Classification on ModelNet40 (Overall Accuracy)
95.1Overall AccuracyADS
Evaluation Results
| Method | Links | |
|---|---|---|
| ADSPublication=ICCV'23, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 95.1 | |
| Mamba3DBackbone=SSM, Number of trainable parameters (#TP)=16.9 (100%), FLOPs (G)=3.9, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 94.7 | |
| Mamba3D + MantisBackbone=SSM, Number of trainable parameters (#TP)=0.8 (4.7%), FLOPs (G)=4.0, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 94.7 | |
| PointMLPPublication=ICLR'22, Tunable Params.=13.2 M, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 94.5 | |
| RepSurf-UPublication=CVPR'22, Tunable Params.=1.5 M, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 94.4 | |
| Mamba3D + PMABackbone=SSM, Number of trainable parameters (#TP)=1.3 (7.7%), FLOPs (G)=4.7, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 94.2 | |
| ReConBackbone=Attn., Number of trainable parameters (#TP)=43.6 (100%), FLOPs (G)=5.3, Evaluation Protocol=Parameter-Efficient Fine-tuning, Data Modality (Cross-modal vs Single-modal)=cross-modal, Augmentation Strategy=Simple rotational augmentation2026.05 | 94.1 | |
| PointNeXtPublication=NeurIPS'22, Tunable Params.=1.4 M, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 94 | |
| Point-M2AEPublication=NeurIPS 22, Tunable Params.=15.3 M, Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Full Fine-Tuning)2025.04 | 94 | |
| Point-M2AEBackbone=Attn., Number of trainable parameters (#TP)=15.3, FLOPs (G)=3.6, Evaluation Protocol=Full Fine-tuning2026.05 | 94 | |
| Joint-MAEBackbone=Attn., Evaluation Protocol=Full Fine-tuning2026.05 | 94 | |
| ReCon + GAPromptBackbone=Attn., Number of trainable parameters (#TP)=0.6 (1.4%), FLOPs (G)=5.0, Evaluation Protocol=Parameter-Efficient Fine-tuning, Data Modality (Cross-modal vs Single-modal)=cross-modal, Augmentation Strategy=Simple rotational augmentation2026.05 | 94 | |
| RECONPublication=ICML 23, Tunable Params.=43.6 M, Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Full Fine-Tuning)2025.04 | 93.9 | |
| MVTNPublication=ICCV'21, Tunable Params.=11.2 M, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 93.8 | |
| MaskPointPublication=ECCV'22, Tunable Params.=22.1 M, Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Full Fine-Tuning)2025.04 | 93.8 | |
| Point-MAEPublication=ECCV'22, Tunable Params.=22.1 M, Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Full Fine-Tuning)2025.04 | 93.8 | |
| Point-MAEBackbone=Attn., Number of trainable parameters (#TP)=22.1 (100%), FLOPs (G)=4.8, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 93.8 | |
| ACTPublication=ICLR'23, Tunable Params.=22.1 M, Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Full Fine-Tuning)2025.04 | 93.7 | |
| ZigzagPointMamba + MantisBackbone=SSM, Number of trainable parameters (#TP)=0.6 (4.9%), FLOPs (G)=4.5, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 93.7 | |
| DC-CCNN++2026.06 | 93.7 | |
| ACTBackbone=Attn., Number of trainable parameters (#TP)=22.1, FLOPs (G)=4.8, Evaluation Protocol=Full Fine-tuning, Augmentation Strategy=Simple rotational augmentation2026.05 | 93.6 | |
| PointMambaBackbone=SSM, Number of trainable parameters (#TP)=12.3 (100%), FLOPs (G)=3.1, Evaluation Protocol=Parameter-Efficient Fine-tuning, Augmentation Strategy=Simple rotational augmentation2026.05 | 93.6 | |
| PointMamba + PMABackbone=SSM, Number of trainable parameters (#TP)=1.1 (8.9%), FLOPs (G)=5.2, Evaluation Protocol=Parameter-Efficient Fine-tuning, Augmentation Strategy=Simple rotational augmentation2026.05 | 93.6 | |
| PointMambainput points=1,024, voting mechanism=without2026.05 | 93.6 | |
| Point-MAE + DAPTPublication=CVPR 24, Tunable Params.=1.1 M (4.97%), Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Parameter-Efficient Fine-Tuning)2025.04 | 93.5 | |
| ReCon + DAPTBackbone=Attn., Number of trainable parameters (#TP)=1.1 (2.5%), FLOPs (G)=5.0, Evaluation Protocol=Parameter-Efficient Fine-tuning, Data Modality (Cross-modal vs Single-modal)=cross-modal, Augmentation Strategy=Simple rotational augmentation2026.05 | 93.5 | |
| PointMamba + MantisBackbone=SSM, Number of trainable parameters (#TP)=0.6 (4.9%), FLOPs (G)=4.0, Evaluation Protocol=Parameter-Efficient Fine-tuning, Augmentation Strategy=Simple rotational augmentation2026.05 | 93.5 | |
| DC-CCNN2026.06 | 93.5 | |
| I2P-MAEBackbone=Attn., Number of trainable parameters (#TP)=15.3, Evaluation Protocol=Full Fine-tuning, Augmentation Strategy=Simple rotational augmentation2026.05 | 93.4 | |
| PCMBackbone=SSM, Number of trainable parameters (#TP)=34.2, Evaluation Protocol=Full Fine-tuning2026.05 | 93.4 | |
| Point Mamba (w/ PCT)input points=1,024, voting mechanism=without2026.05 | 93.4 | |
| PCMinput points=1,024, voting mechanism=without2026.05 | 93.4 | |
| Point-MAE + IDPTPublication=ICCV'23, Tunable Params.=1.7 M (7.69%), Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Parameter-Efficient Fine-Tuning)2025.04 | 93.3 | |
| Point-MAE + Point LoRAPublication=Ours, Tunable Params.=0.77 M (3.43%), Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Parameter-Efficient Fine-Tuning)2025.04 | 93.3 | |
| PointGPT-SBackbone=Attn., Number of trainable parameters (#TP)=29.2, FLOPs (G)=5.7, Evaluation Protocol=Full Fine-tuning, Augmentation Strategy=Simple rotational augmentation2026.05 | 93.3 | |
| Point-MAE + IDPTBackbone=Attn., Number of trainable parameters (#TP)=1.7 (7.7%), FLOPs (G)=7.2, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 93.3 | |
| Point-MAE + PointLoRABackbone=Attn., Number of trainable parameters (#TP)=0.8 (3.6%), Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 93.3 | |
| Point-BERTPublication=CVPR 22, Tunable Params.=22.1 M, Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Full Fine-Tuning)2025.04 | 93.2 | |
| Point-MAE (Full-FT)Publication=ECCV'22, Tunable Params.=22.1 M (100%), Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Parameter-Efficient Fine-Tuning)2025.04 | 93.2 | |
| Point-MAE + PPT*Publication=arXiv'24, Tunable Params.=1.04 M (4.57%), Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Parameter-Efficient Fine-Tuning)2025.04 | 93.2 | |
| Point-BERTBackbone=Attn., Number of trainable parameters (#TP)=22.1, FLOPs (G)=4.8, Evaluation Protocol=Full Fine-tuning2026.05 | 93.2 | |
| ZigzagPointMambaBackbone=SSM, Number of trainable parameters (#TP)=12.3 (100%), FLOPs (G)=3.1, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 93.2 | |
| PCTinput points=1,024, voting mechanism=without2026.05 | 93.2 | |
| DensePoint2026.06 | 93.2 | |
| PosPool2026.06 | 93.2 | |
| PCT2026.06 | 93.2 | |
| PointMLP2026.06 | 93.2 | |
| DGCNNPublication=TOG' 19, Tunable Params.=1.8 M, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 92.9 | |
| DGCNNBackbone=-, Number of trainable parameters (#TP)=1.8, FLOPs (G)=2.4, Evaluation Protocol=Supervised Learning Only, Data Modality (Cross-modal vs Single-modal)=single-modal2026.05 | 92.9 | |
| DGCNNinput points=1,024, voting mechanism=without2026.05 | 92.9 | |
| RS-CNNinput points=1,024, voting mechanism=without2026.05 | 92.9 | |
| PointASNLinput points=1,024, voting mechanism=without2026.05 | 92.9 | |
| RelFlexformer (w/ PCT)input points=1,024, voting mechanism=without2026.05 | 92.9 | |
| RS-CNN2026.06 | 92.9 | |
| PointASNL2026.06 | 92.9 | |
| MLMSPT2026.06 | 92.9 | |
| ZigzagPointMamba + PMABackbone=SSM, Number of trainable parameters (#TP)=1.1 (8.9%), FLOPs (G)=5.7, Evaluation Protocol=Parameter-Efficient Fine-tuning2026.05 | 92.8 | |
| Point Trans2026.06 | 92.8 | |
| SI-MambaBackbone=SSM, Number of trainable parameters (#TP)=12.3, FLOPs (G)=3.6, Evaluation Protocol=Full Fine-tuning2026.05 | 92.7 | |
| OctFormerinput points=1,024, voting mechanism=without2026.05 | 92.7 | |
| Point Mamba (w/ OctFormer)input points=1,024, voting mechanism=without2026.05 | 92.7 | |
| P2Sequenceinput points=1,024, voting mechanism=without2026.05 | 92.6 | |
| RelFlexformer (w/ PCT) + PointRoPEinput points=1,024, voting mechanism=without2026.05 | 92.6 | |
| PointCNNinput points=1,024, voting mechanism=without2026.05 | 92.5 | |
| PointConvinput points=1,024, voting mechanism=without2026.05 | 92.5 | |
| PCNNinput points=1,024, voting mechanism=without2026.05 | 92.3 | |
| Performer (w/ PCT)input points=1,024, voting mechanism=without2026.05 | 92.3 | |
| PointCNNBackbone=-, Number of trainable parameters (#TP)=0.6, FLOPs (G)=0.9, Evaluation Protocol=Supervised Learning Only, Data Modality (Cross-modal vs Single-modal)=single-modal2026.05 | 92.2 | |
| OcCoPublication=ICCV'21, Tunable Params.=22.1 M, Points Num.=1k, Evaluation Protocol=Self-Supervised Representation Learning (Full Fine-Tuning)2025.04 | 92.1 | |
| PointGridinput points=1,024, voting mechanism=without2026.05 | 92 | |
| PointNet++Publication=NeurIPS'17, Tunable Params.=1.5 M, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 90.7 | |
| PointNet++Backbone=-, Number of trainable parameters (#TP)=1.5, FLOPs (G)=1.7, Evaluation Protocol=Supervised Learning Only, Data Modality (Cross-modal vs Single-modal)=single-modal2026.05 | 90.7 | |
| PointNet++input points=1,024, voting mechanism=without2026.05 | 90.7 | |
| A-SCNinput points=1,024, voting mechanism=without2026.05 | 89.9 | |
| PointNetPublication=CVPR'17, Tunable Params.=3.5 M, Points Num.=1k, Evaluation Protocol=Traditional Supervised Learning Only2025.04 | 89.2 | |
| PointNetBackbone=-, Number of trainable parameters (#TP)=3.5, FLOPs (G)=0.5, Evaluation Protocol=Supervised Learning Only, Data Modality (Cross-modal vs Single-modal)=cross-modal2026.05 | 89.2 | |
| PointNetinput points=1,024, voting mechanism=without2026.05 | 89.2 | |
| Spiking PointNet2026.06 | 83.5 |