The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Colored Point Cloud Registration Revisited
Abstract
We present an algorithm for aligning two colored point clouds. The key idea is to optimize a joint photometric and geometric objective that locks the alignment along both the normal direction and the tangent plane. We extend a photometric objective for aligning RGB-D images to point clouds, by locally parameterizing the point cloud with a virtual camera. Experiments demonstrate that our algorithm is more accurate and more robust than prior point cloud registration algorithms, including those that utilize color information. We use the presented algorithms to enhance a state-of-the-art scene reconstruction system. The precision of the resulting system is demonstrated on real-world scenes with accurate ground-truth models.
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Cites
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ORB: An efficient alternative to SIFT or SURF
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3D is here: Point Cloud Library (PCL)
Rusu, R. B., & Cousins, S. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 4,885
KinectFusion: Real-time dense surface mapping and tracking
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A benchmark for the evaluation of RGB-D SLAM systems
Sturm, J., Engelhard, N., Endres, F., Burgard, W., & Cremers, D. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6385773 · Cited by 3,976
Efficient variants of the ICP algorithm
Rusinkiewicz, S., & Levoy, M. (2002) · Proceedings Third International Conference on 3-D Digital Imaging and Modeling · doi:10.1109/im.2001.924423 · Cited by 3,945 · cited 6×
“Notably, point-to-plane ICP has been broadly adopted due to its fast convergence [3, 31].”
BackgroundMethodInfluentialObject modelling by registration of multiple range images
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Cited by
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
3DRegNet: A Deep Neural Network for 3D Point Registration
Pais, G. D., Ramalingam, S., Govindu, V. M., Nascimento, J. C., Chellappa, R., & Miraldo, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00722 · Cited by 268
ConceptFusion: Open-set multimodal 3D mapping
Jatavallabhula, K., Kuwajerwala, A., Gu, Q., Omama, M., Iyer, G., Saryazdi, S., Chen, T., Maalouf, A., Li, S., Keetha, N., et al. (2023) · Robotics: Science and Systems XIX · doi:10.15607/rss.2023.xix.066 · Cited by 228
Kwon, H., Abowd, G. D., & Plötz, T. (2021) · Sensors · doi:10.3390/s21248337 · Cited by 30
Kwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Ploetz, T. (2020) · arXiv · doi:10.48550/arxiv.2006.05675 · Cited by 14
Metadata
- Authors
- Park, J., Zhou, Q.-Y., & Koltun, V.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 143-152
- OpenAlex
- W2776330782
- Cited by
- 331
- References
- 45
- FWCI
- 25.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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