D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
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 lea...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 6358 - 6366 |
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01.06.2020
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Tai, Chiew-Lan Fu, Hongbo Luo, Zixin Bai, Xuyang Quan, Long Zhou, Lei |
| Author_xml | – sequence: 1 givenname: Xuyang surname: Bai fullname: Bai, Xuyang organization: Hong Kong University of Science and Technology – sequence: 2 givenname: Zixin surname: Luo fullname: Luo, Zixin organization: Hong Kong University of Science and Technology – sequence: 3 givenname: Lei surname: Zhou fullname: Zhou, Lei organization: Hong Kong University of Science and Technology – sequence: 4 givenname: Hongbo surname: Fu fullname: Fu, Hongbo organization: City University of Hong Kong – sequence: 5 givenname: Long surname: Quan fullname: Quan, Long organization: Hong Kong University of Science and Technology – sequence: 6 givenname: Chiew-Lan surname: Tai fullname: Tai, Chiew-Lan organization: Hong Kong University of Science and Technology |
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| Snippet | A successful point cloud registration often lies on robust establishment of sparse matches through discriminative 3D local features. Despite the fast evolution... |
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| SubjectTerms | Convolution Detectors Feature extraction Kernel Three-dimensional displays Training Two dimensional displays |
| Title | D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features |
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