Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.

Wang L, Chen Y, Song W, Xu H

DOI: 10.3390/s24061718
Published: 2024 Mar 7
Container: Sensors (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/s24061718,
  title = {Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.},
  author = {Wang L and Chen Y and Song W and Xu H},
  year = {2024},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s24061718},
  url = {https://doi.org/10.3390/s24061718}
}

RIS

TY  - JOUR
TI  - Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.
AU  - Wang L
AU  - Chen Y
AU  - Song W
AU  - Xu H
PY  - 2024
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s24061718
UR  - https://doi.org/10.3390/s24061718
ER  -

APA

L, W., Y, C., W, S., & H, X. (2024). Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s24061718

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pubmed · retrieved 2026-11-10T07:28:06.620Z