Point Cloud Classification on ScanObjectNN PB_T50_RS
90.63Overall AccuracyRecon
Evaluation Results
| Method | Links | |
|---|---|---|
| ReconLearning Paradigm=Cross-Modal Self-Supervised Learning, #Params (M)=44.3, Input Data=2k Points2023.12 | 90.63 | |
| Point-FEMAELearning Paradigm=Single-Modal Self-Supervised Learning, #Params (M)=27.4, Input Data=2k Points2023.12 | 90.22 | |
| I2P-MAELearning Paradigm=Cross-Modal Self-Supervised Learning, #Params (M)=15.3, Input Data=2k Points2023.12 | 90.11 | |
| PointGPT-BPretrain=GPT, #Params(M)=120.5, FLOPs(G)=36.2, Evaluation Setting=Original Paper2024.05 | 89.6 | |
| P2P-HorNetLearning Paradigm=Supervised Learning Only, #Params (M)=195.8, Input Data=40 Images2023.12 | 89.3 | |
| Point-MAE w/ LCMPretrain=MPM, #Params(M)=2.7, FLOPs(G)=1.32024.05 | 88.87 | |
| Point-BERT w/ LCMPretrain=MPM, #Params(M)=3.1, FLOPs(G)=2.52024.05 | 88.57 | |
| ACT w/ LCMPretrain=MPM, #Params(M)=3.1, FLOPs(G)=2.82024.05 | 88.57 | |
| PointMLP + TAPBackbone=PointMLP, Pre-training=TAP2023.07 | 88.5 | |
| Point-M2AE w/ LCMPretrain=MPM, #Params(M)=2.5, FLOPs(G)=6.72024.05 | 88.38 | |
| Point-MAEPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.8, Evaluation Setting=Reproduced downstream setting2024.05 | 88.27 | |
| ACTLearning Paradigm=Cross-Modal Self-Supervised Learning, #Params (M)=22.1, Input Data=2k Points2023.12 | 88.21 | |
| ACTPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.8, Evaluation Setting=Original Paper2024.05 | 88.21 | |
| PointMambaPretrain=MPM, #Params(M)=12.3, Evaluation Setting=Original Paper2024.05 | 88.17 | |
| Point-M2AEPretrain=MPM, #Params(M)=12.9, FLOPs(G)=7.9, Evaluation Setting=Reproduced downstream setting2024.05 | 88.06 | |
| Point-BERTPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.5, Evaluation Setting=Reproduced downstream setting2024.05 | 87.91 | |
| SFRLearning Paradigm=Supervised Learning Only, Input Data=20 Images2023.12 | 87.8 | |
| SFRPretrain=X, Evaluation Setting=Original Paper2024.05 | 87.8 | |
| LCMPretrain=X, #Params(M)=2.7, FLOPs(G)=1.32024.05 | 87.75 | |
| MaskPoint w/ LCMPretrain=MPM, #Params(M)=3.1, FLOPs(G)=2.52024.05 | 87.75 | |
| MaskFeat3DBackbone=PointViT, Evaluation Protocol=fine-tuning2025.12 | 87.7 | |
| MaskPointPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.5, Evaluation Setting=Reproduced downstream setting2024.05 | 87.65 | |
| ACTPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.8, Evaluation Setting=Reproduced downstream setting2024.05 | 87.52 | |
| PointMLPBackbone=PointMLP, Pre-training=None2023.07 | 87.4 | |
| PointNet++ + TAPBackbone=PointNet++, Pre-training=TAP2023.07 | 86.8 | |
| TransformerPretrain=X, #Params(M)=22.1, FLOPs(G)=4.8, Evaluation Setting=Reproduced downstream setting2024.05 | 86.65 | |
| DGCNN + TAPBackbone=DGCNN, Pre-training=TAP2023.07 | 86.6 | |
| Point-M2AELearning Paradigm=Single-Modal Self-Supervised Learning, #Params (M)=15.3, Input Data=2k Points2023.12 | 86.43 | |
| Point-M2AEPretrain=MPM, #Params(M)=12.9, FLOPs(G)=7.9, Evaluation Setting=Original Paper2024.05 | 86.43 | |
| PointNet++Backbone=PointNet++, Pre-training=None2023.07 | 86.2 | |
| DGCNNBackbone=DGCNN, Pre-training=None2023.07 | 86.1 | |
| Joint-MAELearning Paradigm=Cross-Modal Self-Supervised Learning, Input Data=2k Points2023.12 | 86.07 | |
| baselinec=642025.11 | 86.05 | |
| MaskSurfelBackbone=PointViT, Evaluation Protocol=fine-tuning2025.12 | 85.7 | |
| TAP (Ours)Backbone=Standard Transformer, Pre-training=Generative2023.07 | 85.67 | |
| Inter-MAEPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.8, Evaluation Setting=Original Paper2024.05 | 85.4 | |
| PointMLPLearning Paradigm=Supervised Learning Only, #Params (M)=12.6, Input Data=1k Points2023.12 | 85.2 | |
| PointMLPPretrain=X, #Params(M)=12.6, FLOPs(G)=31.4, Evaluation Setting=Original Paper2024.05 | 85.2 | |
| PointMAEBackbone=PointViT, Evaluation Protocol=fine-tuning2025.12 | 85.2 | |
| Point-MAEBackbone=Standard Transformer, Pre-training=Generative2023.07 | 85.18 | |
| Point-MAELearning Paradigm=Single-Modal Self-Supervised Learning, #Params (M)=22.1, Input Data=2k Points2023.12 | 85.18 | |
| Point-MAEPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.8, Evaluation Setting=Original Paper2024.05 | 85.18 | |
| Point-MAE w/ DAPTPretrain=MPM, #Params(M)=22.7, FLOPs(G)=5.0, Evaluation Setting=Original Paper2024.05 | 85.08 | |
| Point-MAE w/ IDPTPretrain=MPM, #Params(M)=23.3, FLOPs(G)=7.1, Evaluation Setting=Original Paper2024.05 | 84.94 | |
| MaskPointBackbone=Standard Transformer, Pre-training=Generative2023.07 | 84.3 | |
| MaskPointLearning Paradigm=Single-Modal Self-Supervised Learning, #Params (M)=22.1, Input Data=2k Points2023.12 | 84.3 | |
| MaskPointPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.5, Evaluation Setting=Original Paper2024.05 | 84.3 | |
| MaskDiscrBackbone=PointViT, Evaluation Protocol=fine-tuning2025.12 | 84.3 | |
| PointBERTBackbone=PointViT, Evaluation Protocol=fine-tuning2025.12 | 83.1 | |
| Point-BERTBackbone=Standard Transformer, Pre-training=Generative2023.07 | 83.07 | |
| Point-BERTLearning Paradigm=Single-Modal Self-Supervised Learning, #Params (M)=22.1, Input Data=1k Points2023.12 | 83.07 | |
| Point-BERTPretrain=MPM, #Params(M)=22.1, FLOPs(G)=4.5, Evaluation Setting=Original Paper2024.05 | 83.07 | |
| MVTNLearning Paradigm=Supervised Learning Only, #Params (M)=11.2, Input Data=20 Images2023.12 | 82.8 | |
| PointMambaPretrain=X, #Params(M)=12.3, Evaluation Setting=Original Paper2024.05 | 82.48 | |
| TransformerPretrain=X, #Params(M)=22.1, FLOPs(G)=4.8, Evaluation Setting=Original Paper2024.05 | 80.78 | |
| SimpleViewLearning Paradigm=Supervised Learning Only, Input Data=6 Images2023.12 | 80.5 | |
| TetraSphereRobustness Category=Rotation-robust, K=4, Rotation Protocol=z/z2022.11 | 79.2 | |
| TetraSphereRobustness Category=Rotation-robust, K=4, Rotation Protocol=z/SO(3)2022.11 | 79.2 | |
| TetraSphereRobustness Category=Rotation-robust, K=2, Rotation Protocol=SO(3)/SO(3)2022.11 | 79 | |
| TetraSphereRobustness Category=Rotation-robust, K=4, Rotation Protocol=SO(3)/SO(3)2022.11 | 79 | |
| TetraSphereRobustness Category=Rotation-robust, K=8, Rotation Protocol=SO(3)/SO(3)2022.11 | 79 | |
| TetraSphereRobustness Category=Rotation-robust, K=16, Rotation Protocol=SO(3)/SO(3)2022.11 | 79 | |
| TetraSphereRobustness Category=Rotation-robust, K=2, Rotation Protocol=z/z2022.11 | 78.9 | |
| TetraSphereRobustness Category=Rotation-robust, K=2, Rotation Protocol=z/SO(3)2022.11 | 78.9 | |
| TetraSphereRobustness Category=Rotation-robust, K=16, Rotation Protocol=z/z2022.11 | 78.8 | |
| TetraSphereRobustness Category=Rotation-robust, K=16, Rotation Protocol=z/SO(3)2022.11 | 78.8 | |
| OcCoBackbone=Standard Transformer, Pre-training=Generative2023.07 | 78.79 | |
| TetraSphereRobustness Category=Rotation-robust, K=1, Rotation Protocol=SO(3)/SO(3)2022.11 | 78.7 | |
| TetraSphereRobustness Category=Rotation-robust, K=8, Rotation Protocol=z/z2022.11 | 78.7 | |
| TetraSphereRobustness Category=Rotation-robust, K=8, Rotation Protocol=z/SO(3)2022.11 | 78.7 | |
| PointCNNRobustness Category=Rotation-sensitive, Rotation Protocol=z/z2022.11 | 78.5 | |
| VN-DGCNNRobustness Category=Rotation-robust, Rotation Protocol=SO(3)/SO(3)2022.11 | 78.5 | |
| TetraSphereRobustness Category=Rotation-robust, K=1, Rotation Protocol=z/z2022.11 | 78.5 | |
| TetraSphereRobustness Category=Rotation-robust, K=1, Rotation Protocol=z/SO(3)2022.11 | 78.5 | |
| DGCNNLearning Paradigm=Supervised Learning Only, #Params (M)=1.8, Input Data=1k Points2023.12 | 78.1 | |
| DGCNNRobustness Category=Rotation-sensitive, Rotation Protocol=z/z2022.11 | 78.1 | |
| PointNet++Learning Paradigm=Supervised Learning Only, #Params (M)=1.5, Input Data=1k Points2023.12 | 77.9 | |
| PointNet++Pretrain=X, #Params(M)=1.5, FLOPs(G)=1.7, Evaluation Setting=Original Paper2024.05 | 77.9 | |
| VN-DGCNNRobustness Category=Rotation-robust, Rotation Protocol=z/z2022.11 | 77.9 | |
| VN-DGCNNRobustness Category=Rotation-robust, Rotation Protocol=z/SO(3)2022.11 | 77.9 | |
| Yu et al.Robustness Category=Rotation-robust, Rotation Protocol=SO(3)/SO(3)2022.11 | 77.4 | |
| w/o pre-trainingBackbone=Standard Transformer, Pre-training=None2023.07 | 77.24 | |
| PointViTBackbone=PointViT, Evaluation Protocol=from scratch2025.12 | 77.2 | |
| Yu et al.Robustness Category=Rotation-robust, Rotation Protocol=z/z2022.11 | 77.2 | |
| Yu et al.Robustness Category=Rotation-robust, Rotation Protocol=z/SO(3)2022.11 | 77.2 | |
| Li et al.Robustness Category=Rotation-robust, Rotation Protocol=SO(3)/SO(3)2022.11 | 74.9 | |
| Li et al.Robustness Category=Rotation-robust, Rotation Protocol=z/z2022.11 | 74.6 | |
| Li et al.Robustness Category=Rotation-robust, Rotation Protocol=z/SO(3)2022.11 | 74.6 | |
| 3D-GFERobustness Category=Rotation-robust, Rotation Protocol=z/z2022.11 | 73.5 | |
| 3D-GFERobustness Category=Rotation-robust, Rotation Protocol=SO(3)/SO(3)2022.11 | 73.5 | |
| 3D-GFERobustness Category=Rotation-robust, Rotation Protocol=z/SO(3)2022.11 | 72.7 | |
| PaRINetRobustness Category=Rotation-robust, Rotation Protocol=SO(3)/SO(3)2022.11 | 72.2 | |
| PaRINetRobustness Category=Rotation-robust, Rotation Protocol=z/z2022.11 | 71.6 | |
| PaRINetRobustness Category=Rotation-robust, Rotation Protocol=z/SO(3)2022.11 | 71.6 | |
| PointNetLearning Paradigm=Supervised Learning Only, #Params (M)=3.5, Input Data=1k Points2023.12 | 68 | |
| PointNetPretrain=X, #Params(M)=3.5, FLOPs(G)=0.5, Evaluation Setting=Original Paper2024.05 | 68 | |
| DGCNNRobustness Category=Rotation-sensitive, Rotation Protocol=SO(3)/SO(3)2022.11 | 63.4 | |
| PointCNNRobustness Category=Rotation-sensitive, Rotation Protocol=SO(3)/SO(3)2022.11 | 51.8 | |
| DGCNNRobustness Category=Rotation-sensitive, Rotation Protocol=z/SO(3)2022.11 | 16.1 | |
| PointCNNRobustness Category=Rotation-sensitive, Rotation Protocol=z/SO(3)2022.11 | 14.9 |