The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-2/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 551–556, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-551-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 551–556, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-551-2022

  25 Feb 2022

25 Feb 2022

SHAPE PRESERVING NOISE ATTENUATION MODEL FOR 3-D-MODELING OF HERITAGE SITES BY PORTABLE LASER SCANS

T. Zhang, J. Abu-Hani, and S. Filin T. Zhang et al.
  • Mapping and Geo-Information Engineering, Technion – Israel Institute of Technology, Haifa, Israel

Keywords: Point clouds, Noise attenuation, 3-D modeling, Laser scanning, Portable laser scanners, Cultural Heritage

Abstract. The development of efficient strategies to document cultural heritage sites is an active research field. One promising avenue to address that need may be found in the use of portable laser scanners. Such scans provide a mapping-grade level of accuracy, yet their level of characterization is limited by the low resolution of the generated point cloud and by the relatively noisy measurements. In this paper we study methods to attenuate the noisy responses as a means to improve the data quality and highlight the underlying structure. Unlike the prevailing plane-fitting-based filtering approaches that tend to blur salient features, we consider the use of local structure properties in our denoising strategy. We use the normal-based bilateral filtering of point clouds as a platform, yet introduce new normal preservation concepts whose incorporation significantly improves the overall denoising process performance. Results demonstrate how our proposed solution outperforms the standard plane-filtering and the naive bilateral approach. The attenuation we achieve yields a more visually pleasing entity description as well as simplified processing of subsequent procedures, including feature extraction and semantic segmentation.