3D shape recognition on ModelNet10
98.46AccuracyRotationNet
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| RotationNetRep.=2D Projection2021.10 | 98.46 | — | — | |
| SPNet_VERep.=2D Projection2021.10 | 97.25 | — | — | |
| PANORAMA-ENNRep.=2D Projection2021.10 | 96.85 | — | — | |
| PointMACESymmetry=O(3), Base Architecture=PointNet++2023.05 | 96.1 | — | — | |
| SO-NetRep.=Point cloud2021.10 | 95.5 | — | — | |
| PointNet ++2023.05 | 95 | — | — | |
| PolyNet (PTQ, √3), (d=2)Rep.=Polygon Mesh, Subsampling=PTQ, √3, Polynomial Degree=22021.10 | 94.93 | — | — | |
| PCNNRep.=Point cloud2021.10 | 94.9 | — | — | |
| PolyNet (√3), (d=2)Rep.=Polygon Mesh, Subsampling=√3, Polynomial Degree=22021.10 | 94.52 | — | — | |
| LP-3DCNNRep.=voxel grid2021.10 | 94.4 | — | — | |
| KCNetRep.=Point cloud2021.10 | 94.4 | — | — | |
| PolyNet (PTQ), (d=2)Rep.=Polygon Mesh, Subsampling=PTQ, Polynomial Degree=22021.10 | 94.4 | — | — | |
| PointNet2023.05 | 94.2 | — | — | |
| VRNRep.=voxel grid2021.10 | 93.61 | — | — | |
| FusionNetRep.=voxel grid2021.10 | 93.11 | — | — | |
| VoxNetRep.=voxel grid2021.10 | 92 | — | — | |
| Cross-atlasRep.=Polygon Mesh2021.10 | 91.2 | — | — | |
| FMixModel=PointNet2020.02 | 89.57 | — | — | |
| BaselineModel=PointNet2020.02 | 89.1 | — | — | |
| Geometry ImageRep.=Polygon Mesh2021.10 | 88.4 | — | — | |
| DeepPanoRep.=2D Projection2021.10 | 85.45 | — | — | |
| 3D ShapeNetsRep.=voxel grid2021.10 | 83.54 | — | — | |
| SPHRep.=Polygon Mesh2021.10 | 79.79 | — | — | |
| KC-NetInput=1024 x 32018.11 | — | — | 94.4 | |
| Kd-NetInput=2^15 x 32018.11 | — | 93.5 | 94 | |
| Point2SequenceInput=1024 x 32018.11 | — | 95.1 | 95.3 | |
| SO-NetInput=2048 x 32018.11 | — | 93.9 | 94.1 |