Volume IV-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 113-120, 2018
https://doi.org/10.5194/isprs-annals-IV-2-113-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 113-120, 2018
https://doi.org/10.5194/isprs-annals-IV-2-113-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  28 May 2018

28 May 2018

LEARNED COMPACT LOCAL FEATURE DESCRIPTOR FOR TLS-BASED GEODETIC MONITORING OF NATURAL OUTDOOR SCENES

Z. Gojcic, C. Zhou, and A. Wieser Z. Gojcic et al.
  • Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, Switzerland

Keywords: Compact feature descriptor, terrestrial laser scanning (TLS), geomonitoring, neural networks, feature-based methods

Abstract. The advantages of terrestrial laser scanning (TLS) for geodetic monitoring of man-made and natural objects are not yet fully exploited. Herein we address one of the open challenges by proposing feature-based methods for identification of corresponding points in point clouds of two or more epochs. We propose a learned compact feature descriptor tailored for point clouds of natural outdoor scenes obtained using TLS. We evaluate our method both on a benchmark data set and on a specially acquired outdoor dataset resembling a simplified monitoring scenario where we successfully estimate 3D displacement vectors of a rock that has been displaced between the scans. We show that the proposed descriptor has the capacity to generalize to unseen data and achieves state-of-the-art performance while being time efficient at the matching step due the low dimension.