The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Edge-aware point set resampling
Abstract
Points acquired by laser scanners are not intrinsically equipped with normals, which are essential to surface reconstruction and point set rendering using surfels. Normal estimation is notoriously sensitive to noise. Near sharp features, the computation of noise-free normals becomes even more challenging due to the inherent undersampling problem at edge singularities. As a result, common edge-aware consolidation techniques such as bilateral smoothing may still produce erroneous normals near the edges. We propose a resampling approach to process a noisy and possibly outlier-ridden point set in an edge-aware manner. Our key idea is to first resample away from the edges so that reliable normals can be computed at the samples, and then based on reliable data, we progressively resample the point set while approaching the edge singularities. We demonstrate that our Edge-Aware Resampling (EAR) algorithm is capable of producing consolidated point sets with noise-free normals and clean preservation of sharp features. We also show that EAR leads to improved performance of edge-aware reconstruction methods and point set rendering techniques.
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Citations
Cites
Reconstruction and representation of 3D objects with radial basis functions
Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., & Evans, T. R. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383266 · Cited by 1,852 · cited 1×
“2006] and RBF [Carr et al. 2001] are guided by normal information and the well-known surfel-based point set rendering [Pfister et al.”
Method
Cited by
PU-Net: Point Cloud Upsampling Network
Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., & Heng, P.-A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00295 · Cited by 697
Self-citationPCPN et Learning Local Shape Properties from Raw Point Clouds
Guerrero, P., Kleiman, Y., Ovsjanikov, M., & Mitra, N. J. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13343 · Cited by 369
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling
Liu, H., Yuan, H., Hou, J., Hamzaoui, R., & Gao, W. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3222918 · Cited by 339
Patch-Based Progressive 3D Point Set Upsampling
Yifan, W., Wu, S., Huang, H., Cohen-Or, D., & Sorkine-Hornung, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00611 · Cited by 317
Self-citationEC-Net: An Edge-Aware Point Set Consolidation Network
Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., & Heng, P.-A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_24 · Cited by 300
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Metadata
- Authors
- Huang, H., Wu, S., Gong, M., Cohen-Or, D., Ascher, U., & Zhang, H. (.
- Venue
- ACM Transactions on Graphics
- Published
- 2013-01
- Volume
- 32
- Issue
- 1
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2169611956
- Cited by
- 452
- References
- 1
- FWCI
- 12.24
- Type
- journal article
- Language
- en
- Open access
- closed
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- Huang, H., Wu, S., Gong, M., Cohen-Or, D., Ascher, U., & Zhang, H. (. (2013). Edge-aware point set resampling. ACM Transactions on Graphics. https://doi.org/10.1145/2421636.2421645
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