3DRegNet: A Deep Neural Network for 3D Point Registration
| Autor: | Pais, G. Dias, Ramalingam, Srikumar, Govindu, Venu Madhav, Nascimento, Jacinto C., Chellappa, Rama, Miraldo, Pedro |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 |
| Druh dokumentu: | Working Paper |
| Popis: | We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available. Comment: 15 pages, 8 figures, 6 tables |
| Databáze: | arXiv |
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