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Aligning point cloud views using persistent feature histograms
Abstract
In this paper we investigate the usage of persistent point feature histograms for the problem of aligning point cloud data views into a consistent global model. Given a collection of noisy point clouds, our algorithm estimates a set of robust 16D features which describe the geometry of each point locally. By analyzing the persistence of the features at different scales, we extract an optimal set which best characterizes a given point cloud. The resulted persistent features are used in an initial alignment algorithm to estimate a rigid transformation that approximately registers the input datasets. The algorithm provides good starting points for iterative registration algorithms such as ICP (Iterative Closest Point), by transforming the datasets to its convergence basin. We show that our approach is invariant to pose and sampling density, and can cope well with noisy data coming from both indoor and outdoor laser scans.
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Cited by
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
Towards 3D Point cloud based object maps for household environments
Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.08.005 · Cited by 1,084
Self-citationA Comprehensive Performance Evaluation of 3D Local Feature Descriptors
Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J., & Kwok, N. M. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0824-y · Cited by 555
SoftGroup for 3D Instance Segmentation on Point Clouds
Vu, T., Kim, K., Luu, T. M., Nguyen, T., & Yoo, C. D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00273 · Cited by 279
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
Ding, L., & Feng, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00885 · Cited by 79
Metadata
- Authors
- Rusu, R. B., Blodow, N., Marton, Z. C., & Beetz, M.
- Venue
- 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 3384-3391
- OpenAlex
- W2098764590
- Cited by
- 1,034
- References
- 0
- FWCI
- 9.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rusu, R. B., Blodow, N., Marton, Z. C., & Beetz, M. (2008). Aligning point cloud views using persistent feature histograms. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2008.4650967
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