Volume II-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 57-64, 2014
https://doi.org/10.5194/isprsannals-II-3-57-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 57-64, 2014
https://doi.org/10.5194/isprsannals-II-3-57-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Aug 2014

07 Aug 2014

Comparison of 3D interest point detectors and descriptors for point cloud fusion

R. Hänsch, T. Weber, and O. Hellwich R. Hänsch et al.
  • Computer Vision & Remote Sensing, Technische University Berlin, Berlin, Germany

Keywords: Keypoint detection, keypoint description, keypoint matching, point cloud fusion, MS Kinect

Abstract. The extraction and description of keypoints as salient image parts has a long tradition within processing and analysis of 2D images. Nowadays, 3D data gains more and more importance. This paper discusses the benefits and limitations of keypoints for the task of fusing multiple 3D point clouds. For this goal, several combinations of 3D keypoint detectors and descriptors are tested. The experiments are based on 3D scenes with varying properties, including 3D scanner data as well as Kinect point clouds. The obtained results indicate that the specific method to extract and describe keypoints in 3D data has to be carefully chosen. In many cases the accuracy suffers from a too strong reduction of the available points to keypoints.