Research map / Point clouds / Point cloud classification
Shape-Oriented Convolution Neural Network for Point Cloud Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add moment-point interactions to intra-shape modeling (ablation) | SOCNN · ModelNet40 | accuracy: 89.5 -> 90.9 | Helped |
| Add moment-point interactions to inter-shape modeling (ablation) | SOCNN · ModelNet40 | accuracy: 88.2 -> 88.7 | Helped |
| Add inter-shape relationship modeling to moment-point intra-shape modeling (ablation) | SOCNN · ModelNet40 | accuracy: 90.9 -> 93.1 | Helped |
| Add intra-shape relationship modeling to moment-point inter-shape modeling (ablation) | SOCNN · ModelNet40 | accuracy: 88.7 -> 93.1 | Helped |
| Increase the point count from 1k to 2k (ablation) | SOCNN with both shape relationships · ModelNet40 | accuracy: 93.1 -> 93.6 | Helped |
Closest papers by meaning
- Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019 · similarity 0.92)
- Attentional ShapeContextNet for Point Cloud Recognition (CVPR 2018 · similarity 0.90)
- A-CNN: Annularly Convolutional Neural Networks on Point Clouds (CVPR 2019 · similarity 0.89)
- PointConv: Deep Convolutional Networks on 3D Point Clouds (CVPR 2019 · similarity 0.89)
- PointCNN: Convolution On X-Transformed Points (NeurIPS 2018 · similarity 0.89)
- Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline (ICML 2021 · similarity 0.89)
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