Research map / Point clouds / Point cloud classification
Grid-GCN for Fast and Scalable Point Cloud Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add coverage weight to the edge relation (ablation) | Grid-GCN · ModelNet40 | OA: 91.1 -> 91.5 | Helped |
| Add grid context pooling (ablation) | Grid-GCN · ModelNet40 | OA: 92.0 -> 92.7 | Helped |
| Increase output feature channels from (64,128,256) to (128,256,512) (ablation) | Grid-GCN³ · ModelNet40 | OA: 92.7 -> 93.1 | Helped |
| Replace CAGQ with naive Grid Query (ablation) | Grid-GCN · ScanNet | OA: 85.4% -> 79.9% | Hurt |
| Reduce node points per group to 0.5×K (ablation) | Grid-GCN with CAGQ · ScanNet | OA: 85.4% -> 83.9% | Hurt |
| Increase input point count to 81920 | Grid-GCN versus PointNet++ · ScanNet | latency: 218.9 -> 19.8 | Helped |
| Add more output channels to the compact Grid-GCN model (ablation) | Grid-GCN on S3DIS · S3DIS area 5 | mIOU: 53.21 -> 57.75 | Helped |
Closest papers by meaning
- PU-GCN: Point Cloud Upsampling Using Graph Convolutional Networks (CVPR 2021 · similarity 0.88)
- GCD-Sampling: A General Cross-scale Decoupled Sampling for Point Cloud (AAAI 2025 · similarity 0.88)
- Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis (CVPR 2020 · similarity 0.88)
- DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning (AAAI 2024 · similarity 0.87)
- LinNet: Linear Network for Efficient Point Cloud Representation Learning (NeurIPS 2024 · similarity 0.87)
- DeepLA-Net: Very Deep Local Aggregation Networks for Point Cloud Analysis (CVPR 2025 · similarity 0.86)
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