Fully Convolutional Geometric Features
Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking. State-of-the-art methods require computing low-level features as input or extracting patch-based features with limited receptive field. In this work, we present fully-convolutional geometric features, computed in a single pass by a 3D fully-convolutional network. We also present new metric learning losses that dramatically improve performance. Fully-convolutional geometric features are compact, capture broad spatial context, and scale to large scenes. We experimentally validate our approach on both indoor and outdoor datasets. Fully-convolutional geometric features achieve state-of-the-art accuracy without requiring prepossessing, are compact (32 dimensions), and are 600 times faster than the most accurate prior method.
PDF Abstract International Conference 2019 PDF International Conference 2019 AbstractCode
KITTI
3DMatch
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Point Cloud Registration | 3DLoMatch (10-30% overlap) | FCGF (reported in PREDATOR) | Recall ( correspondence RMSE below 0.2) | 40.1 | # 8 | |
| Point Cloud Registration | 3DMatch (at least 30% overlapped - sample 5k interest points) | FCGF (reported in PREDATOR) | Recall ( correspondence RMSE below 0.2) | 85.1 | # 7 | |
| Point Cloud Registration | 3DMatch Benchmark | FCGF + RANSAC | Feature Matching Recall | 85 | # 11 | |
| 3D Feature Matching | 3DMatch Benchmark | FCGF | Average Recall | 0.9578 | # 1 | |
| Point Cloud Registration | 3DMatch (trained on KITTI) | FCGF | Recall | 0.325 | # 4 | |
| Point Cloud Registration | ETH (trained on 3DMatch) | FCGF | Feature Matching Recall | 0.161 | # 11 | |
| Point Cloud Registration | KITTI | FCGF | Success Rate | 96.57 | # 5 | |
| Point Cloud Registration | KITTI (trained on 3DMatch) | FCGF | Success Rate | 24.19 | # 14 |
