3D Point Cloud Generation on ShapeNet Chair category (test)
1.92MMD (CD)Training set
Evaluation results
| Method | Links | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Training set2019.06 | 1.92 | — | — | — | 59.67 | 57.25 | — | 1.5 | 7.38 | 55.44 | 58.46 | — | — | |
| Training set2021.03 | 1.92 | — | — | — | — | — | — | — | 7.38 | 55.44 | 58.46 | 59.67 | 57.25 | |
| DPM2022.10 | 2.399 | — | — | — | — | — | — | — | 2.066 | 35.5 | 74.77 | 60.05 | 44.86 | |
| PointFlow2022.10 | 2.409 | — | — | — | — | — | — | — | 1.595 | 50 | 60.57 | 62.84 | 42.9 | |
| PointFlowFull parameters (M)=1.61, Generative parameters (M)=1.062019.06 | 2.42 | — | — | — | 60.88 | 46.83 | — | 1.74 | 7.87 | 46.98 | 59.89 | — | — | |
| PointFlow# Parameters (Full)=1.61M, # Parameters (Gen)=1.06M2021.03 | 2.42 | — | — | — | — | — | — | — | 7.87 | 46.98 | 59.89 | 60.88 | 46.83 | |
| l-GAN (CD)Full parameters (M)=1.97, Generative parameters (M)=1.712019.06 | 2.46 | — | — | — | 64.43 | 41.39 | — | 4.59 | 8.91 | 25.68 | 85.27 | — | — | |
| l-GAN (CD)# Parameters (Full)=1.97M, # Parameters (Gen)=1.71M2021.03 | 2.46 | — | — | — | — | — | — | — | 8.91 | 25.68 | 85.27 | 64.43 | 41.39 | |
| SoftFlow2022.10 | 2.528 | — | — | — | — | — | — | — | 1.682 | 47.43 | 60.05 | 59.21 | 41.39 | |
| DPM2022.10 | 2.5337 | — | — | — | — | — | — | — | 1.9746 | 35.01 | 74.96 | 61.96 | 47.42 | |
| DPF-Net2022.10 | 2.536 | — | — | — | — | — | — | — | 1.632 | 48.79 | 58.53 | 62 | 44.71 | |
| SetVAE2022.10 | 2.545 | — | — | — | — | — | — | — | 1.585 | 44.26 | 60.57 | 58.84 | 46.83 | |
| SetVAE# Parameters (Full)=0.75M, # Parameters (Gen)=0.39M2021.03 | 2.55 | — | — | — | — | — | — | — | 7.82 | 45.01 | 61.48 | 58.76 | 46.98 | |
| r-GANFull parameters (M)=7.22, Generative parameters (M)=6.912019.06 | 2.57 | — | — | — | 71.75 | 33.99 | — | 11.5 | 12.8 | 9.97 | 99.47 | — | — | |
| l-GAN (CD)2022.10 | 2.589 | — | — | — | — | — | — | — | 2.007 | 29.31 | 83.84 | 68.58 | 41.99 | |
| l-GAN (EMD)Full parameters (M)=1.97, Generative parameters (M)=1.712019.06 | 2.61 | — | — | — | 64.73 | 40.79 | — | 2.27 | 7.85 | 41.69 | 65.56 | — | — | |
| l-GAN (EMD)# Parameters (Full)=1.97M, # Parameters (Gen)=1.71M2021.03 | 2.61 | — | — | — | — | — | — | — | 7.85 | 41.69 | 65.56 | 64.73 | 40.79 | |
| PVD2022.10 | 2.622 | — | — | — | — | — | — | — | 1.556 | 50.6 | 53.32 | 56.26 | 49.84 | |
| LION2022.10 | 2.64 | — | — | — | — | — | — | — | 1.55 | 52.11 | 52.34 | 53.7 | 48.94 | |
| PC-GANFull parameters (M)=9.14, Generative parameters (M)=1.522019.06 | 2.75 | — | — | — | 76.03 | 36.5 | — | 3.9 | 8.2 | 38.98 | 78.37 | — | — | |
| PC-GAN# Parameters (Full)=9.14M, # Parameters (Gen)=1.52M2021.03 | 2.75 | — | — | — | — | — | — | — | 8.2 | 38.98 | 78.37 | 76.03 | 36.5 | |
| l-GAN (EMD)2022.10 | 2.811 | — | — | — | — | — | — | — | 1.619 | 44.86 | 64.65 | 71.9 | 38.07 | |
| LION2022.10 | 2.8561 | — | — | — | — | — | — | — | 1.6898 | 54.51 | 48.67 | 52.07 | 49.78 | |
| train set2022.10 | 2.8793 | — | — | — | — | — | — | — | 1.6867 | 56.13 | 48.97 | 49.11 | 55.1 | |
| IM-GAN2022.10 | 2.8935 | — | — | — | — | — | — | — | 1.732 | 50.81 | 58.2 | 57.09 | 50.96 | |
| PVD2022.10 | 2.9024 | — | — | — | — | — | — | — | 1.7144 | 50.22 | 57.9 | 61.89 | 46.23 | |
| ShapeGF2022.10 | 3.7243 | — | — | — | — | — | — | — | 2.3944 | 44.26 | 61.25 | 58.01 | 48.34 | |
| train set2022.10 | 3.844 | — | — | — | — | — | — | — | 2.3209 | 53.63 | 52.19 | 53.17 | 49.55 | |
| LION2022.10 | 3.8458 | — | — | — | — | — | — | — | 2.3086 | 50.15 | 53.85 | 56.5 | 46.37 | |
| SP-GAN2022.10 | 4.2084 | — | — | — | — | — | — | — | 2.6202 | 32.93 | 83.69 | 72.58 | 40.03 | |
| PDGN2022.10 | 4.2242 | — | — | — | — | — | — | — | 2.5766 | 36.71 | 79 | 71.83 | 43.2 | |
| GCA2022.10 | 4.4035 | — | — | — | — | — | — | — | 2.582 | 47.89 | 64.5 | 64.27 | 45.92 | |
| TreeGAN2022.10 | 4.8409 | — | — | — | — | — | — | — | 3.5047 | 26.59 | 96.37 | 88.37 | 39.88 | |
| r-GAN2022.10 | 5.151 | — | — | — | — | — | — | — | 8.312 | 15.13 | 99.7 | 83.69 | 24.27 | |
| Bridge + StatisticSteps=1002022.09 | 12.25 | — | — | — | — | — | — | — | 1.78 | 47.56 | — | — | 48.39 | |
| Diffusion Probabilistic Models for 3D Point Cloud Generation2021.03 | 12.276 | — | — | — | — | — | — | 7.797 | 1.784 | 47.52 | 69.06 | 60.11 | 48.94 | |
| DPM2022.02 | 12.276 | — | — | — | — | — | — | 7.797 | 1.784 | 47.52 | 69.06 | 60.11 | 48.94 | |
| Bridge + RieszSteps=1002022.09 | 12.31 | — | — | — | — | — | — | — | 1.82 | 47.42 | — | — | 48.14 | |
| DiffusionSteps=1002022.09 | 12.32 | — | — | — | — | — | — | — | 1.79 | 47.59 | — | — | 47.41 | |
| BridgeSteps=1002022.09 | 12.47 | — | — | — | — | — | — | — | 1.85 | 47.13 | — | — | 47.83 | |
| Bridge + StatisticSteps=102022.09 | 12.65 | — | — | — | — | — | — | — | 1.84 | 45.23 | — | — | 47.58 | |
| SnowflakeNet2022.02 | 12.742 | — | — | — | — | — | — | 7.579 | 1.83 | 36.91 | 75.08 | 58.85 | 48.83 | |
| Bridge + RieszSteps=102022.09 | 12.84 | — | — | — | — | — | — | — | 1.95 | 44.31 | — | — | 47.21 | |
| BridgeSteps=102022.09 | 13.04 | — | — | — | — | — | — | — | 2.14 | 42.59 | — | — | 46.01 | |
| ShapeGF2021.03 | 13.175 | — | — | — | — | — | — | 5.996 | 1.785 | 46.71 | 62.69 | 56.17 | 48.53 | |
| ShapeGF2022.02 | 13.175 | — | — | — | — | — | — | 5.996 | 1.785 | 46.71 | 62.69 | 56.17 | 48.53 | |
| PC-GAN2021.03 | 13.436 | — | — | — | — | — | — | 6.649 | 3.104 | 22.14 | 100 | 69.67 | 46.23 | |
| PC-GAN2022.02 | 13.436 | — | — | — | — | — | — | 6.649 | 3.104 | 22.14 | 100 | 69.67 | 46.23 | |
| PointFlow2021.03 | 13.631 | — | — | — | — | — | — | 12.474 | 1.856 | 43.38 | 68.4 | 66.13 | 41.86 | |
| PointFlow2022.02 | 13.631 | — | — | — | — | — | — | 12.474 | 1.856 | 43.38 | 68.4 | 66.13 | 41.86 | |
| Traintype=Oracle/Ground Truth reference2021.03 | 13.954 | — | — | — | — | — | — | 3.602 | 1.756 | 54.9 | 48.28 | 49.14 | 53.29 | |
| DiffusionSteps=102022.09 | 14.01 | — | — | — | — | — | — | — | 3.23 | 29.36 | — | — | 32.72 | |
| TreeGAN2021.03 | 14.936 | — | — | — | — | — | — | 13.282 | 3.613 | 6.77 | 100 | 74.92 | 38.02 | |
| TreeGAN2022.02 | 14.936 | — | — | — | — | — | — | 13.282 | 3.613 | 6.77 | 100 | 74.92 | 38.02 | |
| GCN-GAN2021.03 | 15.354 | — | — | — | — | — | — | 21.708 | 2.213 | 35.09 | 95.8 | 77.86 | 39.84 | |
| GCN-GAN2022.02 | 15.354 | — | — | — | — | — | — | 21.708 | 2.213 | 35.09 | 95.8 | 77.86 | 39.84 | |
| DiffFacto2025.05 | — | 65.23 | 42.5 | 3.27 | — | — | — | — | — | — | — | — | — | |
| DiffFacto2025.05 | — | — | — | — | 77.34 | 35.37 | 20.3 | — | — | — | — | — | — | |
| l-GAN (EMD)2020.06 | — | — | — | — | — | — | — | — | — | — | 65.56 | 64.73 | — | |
| LionBackbone=PointNet++2025.05 | — | — | — | — | 65.42 | 43.75 | 17.13 | — | — | — | — | — | — | |
| LionBackbone=SPoTr2025.05 | — | — | — | — | 65.27 | 44.02 | 16.98 | — | — | — | — | — | — | |
| Lion & PointNet++2025.05 | — | 69.25 | 35.1 | 3.99 | — | — | — | — | — | — | — | — | — | |
| PC-GAN2020.06 | — | — | — | — | — | — | — | — | — | — | 78.37 | 76.03 | — | |
| PointFlow2020.06 | — | — | — | — | — | — | — | — | — | — | 59.89 | 60.88 | — | |
| SeaLion2025.05 | — | 63.24 | 46.5 | 2.73 | — | — | — | — | — | — | — | — | — | |
| SeaLion2025.05 | — | — | — | — | 63.14 | 46.88 | 16.25 | — | — | — | — | — | — | |
| SoftPointFlow2020.06 | — | — | — | — | — | — | — | — | — | — | 63.51 | 59.95 | — |