3D Object Detection On S3Dis
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Paper Title | Repository | ||
|---|---|---|---|---|
| GSDN | 47.8 | 25.1 | Generative Sparse Detection Networks for 3D Single-shot Object Detection | - |
| Point-GCC+TR3D | 75.1 | 56.7 | Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast | - |
| FCAF3D | 66.7 | 45.9 | FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection | - |
| TR3D | 74.5 | 51.7 | TR3D: Towards Real-Time Indoor 3D Object Detection | - |
| Swin3D-L+FCAF3D | 72.1 | 54.0 | Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding | - |
| UniDet3D | 75.2 | 60.8 | UniDet3D: Multi-dataset Indoor 3D Object Detection | - |
| SPGroup3D | 69.2 | 47.2 | SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection | - |
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