Volume XL-1/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 269-274, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W4-269-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 269-274, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W4-269-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Aug 2015

26 Aug 2015

PLANAR CONSTRAINTS FOR AN IMPROVED UAV-IMAGE-BASED DENSE POINT CLOUD GENERATION

F. He1, A. Habib1, and A. Al-Rawabdeh2 F. He et al.
  • 1Lyles School of Civil Engineering Purdue University, 550 Stadium Mall Dr., West Lafayette, IN 47907, USA
  • 2Department of Geomatics Engineering University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada

Keywords: Image Matching, Dense Point Cloud, UAV Images, Segmentation

Abstract. In this paper, we proposed a new refinement procedure for the semi-global dense image matching. In order to remove outliers and improve the disparity image derived from the semi-global algorithm, both the local smoothness constraint and point cloud segments are utilized. Compared with current refinement technique, which usually assumes the correspondences between planar surfaces and 2D image segments, our proposed approach can effectively deal with object with both planar and curved surfaces. Meanwhile, since 3D point clouds contain more precise geometric information regarding to the reconstructed objects, the planar surfaces identified in our approach can be more accurate. In order to illustrate the feasibility of our approach, several experimental tests are conducted on both Middlebury test and real UAV-image datasets. The results demonstrate that our approach has a good performance on improving the quality of the derived dense image-based point cloud.