Data Structure ?
1.9G
Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
ModelNetデータセットには、合計662種類の目標分類、127,915個のCADモデル、および10種類の方向がラベル付けされたデータが含まれており、コンピュータビジョン、コンピュータグラフィックス、ロボット工学、および認知科学の研究者に、包括的な物体の3Dモデルを提供することを目的としています。
このデータセットには、3つのサブセットが含まれています：
ModelNet10は、10種類の方向がラベル付けされたサブセットデータです。
ModelNet40は、40種類のカテゴリの3Dモデルです。
Aligned40は、40種類のラベル付けされた3Dモデルです。
ModelNetデータセットは、プリンストン大学のビジョンとロボット工学研究所によって2015年に公開されました。主な公開者は、N. Sedaghat、M. Zolfaghari、E. Amiri、およびT. Broxです。関連する論文には、「3D ShapeNets: A Deep Representation for Volumetric Shapes」があります。
ModelNetベンチマークリーダーボード
結果の追加または更新を行うには、Shuran Songまでメールでご連絡ください。
メールには、以下の形式で情報を提供してください：
アルゴリズム名、ModelNet40分類、ModelNet40検索、ModelNet10分類、ModelNet10検索
著者リスト、論文タイトル、学会名、論文のリンク。
例：
3D-DescriptorNet、-、-、-、92.4%、-
Jianwen Xie、Zilong Zheng、Ruiqi Gao、Wenguan Wang、Song-Chun Zhu、およびYing Nian Wu、Learning Descriptor Networks for 3D Shape Synthesis and Analysis。CVPR 2018、http://...
| アルゴリズム | ModelNet40 分類 (精度) | ModelNet40 検索 (mAP) | ModelNet10 分類 (精度) | ModelNet10 検索 (mAP) |
|---|---|---|---|---|
| RS-CNN[63] | 93.6% | - | - | - |
| LP-3DCNN[62] | 92.1% | - | 94.4% | - |
| LDGCNN[61] | 92.9% | - | - | - |
| Primitive-GAN[60] | 86.4% | - | 92.2% | - |
| 3DCapsule [59] | 92.7% | - | 94.7% | - |
| 3D2SeqViews [58] | 93.40% | 90.76% | 94.71% | 92.12% |
| OrthographicNet [57] | - | - | 88.56% | 86.85% |
| Ma et al. [56] | 91.05% | 84.34% | 95.29% | 93.19% |
| MLVCNN [55] | 94.16% | 92.84% | - | - |
| iMHL [54] | 97.16% | - | - | - |
| HGNN [53] | 96.6% | - | - | - |
| SPNet [52] | 92.63% | 85.21% | 97.25% | 94.20% |
| MHBN [51] | 94.7 | - | 95.0 | - |
| VIPGAN [50] | 91.98 | 89.23 | 94.05 | 90.69 |
| Point2Sequence [49] | 92.60 | - | 95.30 | - |
| Triplet-Center Loss [48] | - | 88.0% | - | - |
| PVNet[47] | 93.2% | 89.5% | - | - |
| GVCNN[46] | 93.1% | 85.7% | - | - |
| MLH-MV[45] | 93.11% | 94.80% | ||
| MVCNN-New[44] | 95.0% | |||
| SeqViews2SeqLabels[43] | 93.40% | 89.09% | 94.82% | 91.43% |
| G3DNet[42] | 91.13% | 93.1% | ||
| VSL [41] | 84.5% | 91.0% | ||
| 3D-CapsNets[40] | 82.73% | 70.1% | 93.08% | 88.44% |
| KCNet[39] | 91.0% | 94.4% | ||
| FoldingNet[38] | 88.4% | 94.4% | ||
| binVoxNetPlus[37] | 85.47% | 92.32% | ||
| DeepSets[36] | 90.3% | |||
| 3D-DescriptorNet[35] | 92.4% | |||
| SO-Net[34] | 93.4% | 95.7% | ||
| Minto et al.[33] | 89.3% | 93.6% | ||
| RotationNet[32] | 97.37% | 98.46% | ||
| LonchaNet[31] | 94.37 | |||
| Achlioptas et al. [30] | 84.5% | 95.4% | ||
| PANORAMA-ENN [29] | 95.56% | 86.34% | 96.85% | 93.28% |
| 3D-A-Nets [28] | 90.5% | 80.1% | ||
| Soltani et al. [27] | 82.10% | |||
| Arvind et al. [26] | 86.50% | |||
| LonchaNet [25] | 94.37% | |||
| 3DmFV-Net [24] | 91.6% | 95.2% | ||
| Zanuttigh and Minto [23] | 87.8% | 91.5% | ||
| Wang et al. [22] | 93.8% | |||
| ECC [21] | 83.2% | 90.0% | ||
| PANORAMA-NN [20] | 90.7% | 83.5% | 91.1% | 87.4% |
| MVCNN-MultiRes [19] | 91.4% | |||
| FPNN [18] | 88.4% | |||
| PointNet[17] | 89.2% | |||
| Klokov and Lempitsky[16] | 91.8% | 94.0% | ||
| LightNet[15] | 88.93% | 93.94% | ||
| Xu and Todorovic[14] | 81.26% | 88.00% | ||
| Geometry Image [13] | 83.9% | 51.3% | 88.4% | 74.9% |
| Set-convolution [11] | 90% | |||
| PointNet [12] | 77.6% | |||
| 3D-GAN [10] | 83.3% | 91.0% | ||
| VRN Ensemble [9] | 95.54% | 97.14% | ||
| ORION [8] | 93.8% | |||
| FusionNet [7] | 90.8% | 93.11% | ||
| Pairwise [6] | 90.7% | 92.8% | ||
| MVCNN [3] | 90.1% | 79.5% | ||
| GIFT [5] | 83.10% | 81.94% | 92.35% | 91.12% |
| VoxNet [2] | 83% | 92% | ||
| DeepPano [4] | 77.63% | 76.81% | 85.45% | 84.18% |
| 3DShapeNets [1] | 77% | 49.2% | 83.5% | 68.3% |
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- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
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