Research Article
A New Volumetric CNN for 3D Object Classification Based on Joint Multiscale Feature and Subvolume Supervised Learning Approaches
Table 2
Classification results by different methods on ModelNet40 and ModelNet10 datasets.
| Network type | Method | Input | Size | Pretrain | Augmentation | ModelNet40 (%) | ModelNet10 (%) |
| Single volumetric | MS-VDCNN(Ours) | Volumetric | 6.2 M | ModelNet40 | 24 | 92.93 | 95.3 | 3DShapeNets [15] | Volumetric | 38 M | ModelNet40 | 12 | 77.32 | 83.5 | VoxNet [16] | Volumetric | 0.92 M | — | 12 | 83 | 92 | Voxception [56] | Volumetric | — | ModelNet40 | 24 | 90 | 93.28 | OctNet [23] | Volumetric | | — | | 86.5 | 90.9 | VRN [56] | Volumetric | 18 M | ModelNet40 | 24 | 91.33 | 93.61 | Aniprobing [14] | Volumetric | — | — | 60 | 85.6 | — | Ensemble volumetric | NormalNet [17] | Volumetric + norm: vector | 6.5 M | | 20 | 88.8 | 93.1 | VRN ensemble [56] | Volumetric | 90 M | ModelNet40 | 24 | 95.54 | 97.14 | FusionNet [20] | Volumetric + multiview | 118 M | ImageNet ModelNet40 | 60 | 90.80 | 93.1 | Point cloud | PointNet [50] | Points | 0.45 M | — | — | 86.2 | 89.2 | PointNet++ [52] | Points | — | — | — | — | 90.7 | 3D Capsule [55] | Points | — | — | — | 92.7 | 94.7 | RS-CNN [54] | Points | 1.41 | — | — | 93.6 | — | Multiview | MVCNN [11] | Multiview | — | ImageNet | 80 | 90.10 | — | Ma et al. [46] | Multiview | — | ImageNet | 12 | 91.05 | 95.29 | 3D2SeqViews [47] | Multiview | — | ImageNet | 12 | 93.40 | 94.71 |
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