Training Free 3D Point Cloud Classification 1
评估指标
Accuracy (%)
Need 3D Data?
评测结果
各个模型在此基准测试上的表现结果
模型名称 | Accuracy (%) | Need 3D Data? | Paper Title | Repository |
|---|---|---|---|---|
| CLIP2Point | 23.2 | Yes | CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training | |
| PointCLIP | 15.4 | No | PointCLIP: Point Cloud Understanding by CLIP | |
| Point-GN | 86.4 | Yes | Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification | - |
| PointCLIP V2 | 35.4 | No | PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning | |
| Point-NN | 64.9 | Yes | Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis | |
| CALIP | 16.9 | No | CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention |
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