The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Multi‐scale Feature Extraction on Point‐Sampled Surfaces
Open accessAbstract
We present a new technique for extracting line‐type features on point‐sampled geometry. Given an unstructuredpoint cloud as input, our method first applies principal component analysis on local neighborhoods toclassify points according to the likelihood that they belong to a feature. Using hysteresis thresholding, we thencompute a minimum spanning graph as an initial approximation of the feature lines. To smooth out the featureswhile maintaining a close connection to the underlying surface, we use an adaptation of active contour models.Central to our method is a multi‐scale classification operator that allows feature analysis at multiplescales, using the size of the local neighborhoods as a discrete scale parameter. This significantly improves thereliability of the detection phase and makes our method more robust in the presence of noise. To illustrate theusefulness of our method, we have implemented a non‐photorealistic point renderer to visualize point‐sampledsurfaces as line drawings of their extracted feature curves.
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Cites
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Cited by
Weinmann, M., Jutzi, B., Hinz, S., & Mallet, C. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.01.016 · Cited by 709
Scale-invariant heat kernel signatures for non-rigid shape recognition
Bronstein, M. M., & Kokkinos, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539838 · Cited by 686
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey
Guo, Y., Bennamoun, M., Sohel, F., Lu, M., & Wan, J. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2316828 · Cited by 600
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
Hackel, T., Wegner, J. D., & Schindler, K. (2016) · ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences · doi:10.5194/isprs-annals-iii-3-177-2016 · Cited by 371
Reassembling fractured objects by geometric matching
Huang, Q.-X., Flöry, S., Gelfand, N., Hofer, M., & Pottmann, H. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141925 · Cited by 329
Metadata
- Authors
- Pauly, M., Keiser, R., & Gross, M.
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2003-09
- Volume
- 22
- Issue
- 3
- Pages
- 281-289
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W2149887512
- Cited by
- 560
- References
- 19
- FWCI
- 18.33
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
