Papers › D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
Xuyang Bai, Zixin Luo, Lei Zhou, Hongbo Fu, Long Quan, Chiew-Lan Tai
A successful point cloud registration often lies on robust establishment of sparse matches through discriminative 3D local features. Despite the fast evolution of learning-based 3D feature descriptors, little attention has been drawn to the learning of 3D feature detectors, even less for a joint learning of the two tasks. In this paper, we leverage a 3D fully convolutional network for 3D point clouds, and propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point. In particular, we propose a keypoint selection strategy that overcomes the inherent density variations of 3D point clouds, and further propose a self-supervised detector loss guided by the on-the-fly feature matching results during training. Finally, our method achieves state-of-the-art results in both indoor and outdoor scenarios, evaluated on 3DMatch and KITTI datasets, and shows its strong generalization ability on the ETH dataset. Towards practical use, we show that by adopting a reliable feature detector, sampling a smaller number of features is sufficient to achieve accurate and fast point cloud alignment.[code release](https://github.com/XuyangBai/D3Feat)
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Point Cloud Registration | 3DLoMatch (10-30% overlap) | D3Feat (reported in PREDATOR) | Recall ( correspondence RMSE below 0.2) | 37.2 | #10 of 13 | Archive leaderboard | report |
| Point Cloud Registration | 3DMatch (at least 30% overlapped - sample 5k interest points) | D3Feat (reported in PREDATOR) | Recall ( correspondence RMSE below 0.2) | 81.6 | #8 of 11 | Archive leaderboard | report |
| Point Cloud Registration | 3DMatch (trained on KITTI) | D3Feat-pred | Recall | 0.627 | #3 of 5 | Archive leaderboard | report |
| Point Cloud Registration | 3DMatch Benchmark | D3Feat-Pred | Feature Matching Recall | 95.8 | #7 of 15 | Archive leaderboard | report |
| Point Cloud Registration | 3DMatch Benchmark | D3Feat-rand | Feature Matching Recall | 95.3 | #8 of 15 | Archive leaderboard | report |
| Point Cloud Registration | ETH (trained on 3DMatch) | D3Feat-pred | Feature Matching Recall | 0.563 | #7 of 20 | Archive leaderboard | report |
| Point Cloud Registration | KITTI | D3Feat-pred | Success Rate | 99.81 | #2 of 6 | Archive leaderboard | report |
| Point Cloud Registration | KITTI (trained on 3DMatch) | D3Feat-pred | Success Rate | 36.76 | #13 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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