Learning Multiview 3D Point Cloud Registration
| Autor: | Caifa Zhou, Tolga Birdal, Jan Dirk Wegner, Zan Gojcic, Leonidas J. Guibas |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Point cloud 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Machine Learning (cs.LG) Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | CVPR 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Popis: | We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm. Registration of multiple scans typically follows a two-stage pipeline: the initial pairwise alignment and the globally consistent refinement. The former is often ambiguous due to the low overlap of neighboring point clouds, symmetries and repetitive scene parts. Therefore, the latter global refinement aims at establishing the cyclic consistency across multiple scans and helps in resolving the ambiguous cases. In this paper we propose, to the best of our knowledge, the first end-to-end algorithm for joint learning of both parts of this two-stage problem. Experimental evaluation on well accepted benchmark datasets shows that our approach outperforms the state-of-the-art by a significant margin, while being end-to-end trainable and computationally less costly. Moreover, we present detailed analysis and an ablation study that validate the novel components of our approach. The source code and pretrained models are publicly available under https://github.com/zgojcic/3D_multiview_reg. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ISBN:978-1-7281-7168-5 ISBN:978-1-7281-7169-2 |
| Databáze: | OpenAIRE |
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