Name
Abstract | ||
|---|---|---|
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 at https://github.com/3DVisionISR/3DRegNet. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.00722 | 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
DocType | ISSN | Citations |
Conference | 1063-6919 | 5 |
PageRank | References | Authors |
0.40 | 39 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Dias Pais | 1 | 5 | 0.40 |
| S. Ramalingam | 2 | 686 | 37.32 |
| Venu Madhav Govindu | 3 | 394 | 28.05 |
| Jacinto C. Nascimento | 4 | 396 | 40.94 |
| Chellappa Rama | 5 | 3621 | 215.79 |
| Pedro Miraldo | 6 | 74 | 13.56 |