Point Cloud Registration On 3Dmatch Benchmark
评估指标
Feature Matching Recall
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | Feature Matching Recall |
|---|---|
| d3feat-joint-learning-of-dense-detection-and | 95.8 |
| the-perfect-match-3d-point-cloud-matching | 94.7 |
| ppf-foldnet-unsupervised-learning-of-rotation | 71.8 |
| d3feat-joint-learning-of-dense-detection-and | 95.3 |
| imfnet-interpretable-multimodal-fusion-for | 98.6 |
| fully-convolutional-geometric-features | 85 |
| 3d-point-cloud-registration-with-multi-scale | 98.4 |
| distinctive-3d-local-deep-descriptors | 94.8 |
| fast-point-feature-histograms-fpfh-for-3d | 44.2 |
| spinnet-learning-a-general-surface-descriptor | 97.6 |
| 3dmatch-learning-local-geometric-descriptors | 66.8 |
| generalisable-and-distinctive-3d-local-deep | 97.9 |
| points-to-patches-enabling-the-use-of-self | 96.8 |
| end-to-end-learning-local-multi-view | 97.5 |
| ppfnet-global-context-aware-local-features | 62.3 |