The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 391–398, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-391-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 391–398, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-391-2021

  28 Jun 2021

28 Jun 2021

ENHANCING GEOMETRIC EDGE DETAILS IN MVS RECONSTRUCTION

E. K. Stathopoulou1,2, S. Rigon1, R. Battisti1, and F. Remondino1 E. K. Stathopoulou et al.
  • 13D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 2Laboratory of Photogrammetry, School of Rural and Surveying Engineering, National Technical University of Athens, Greece

Keywords: multi view stereo, edge, mesh, point cloud, dense reconstruction, surface

Abstract. Mesh models generated by multi view stereo (MVS) algorithms often fail to represent in an adequate manner the sharp, natural edge details of the scene. The harsh depth discontinuities of edge regions are eventually a challenging task for dense reconstruction, while vertex displacement during mesh refinement frequently leads to smoothed edges that do not coincide with the fine details of the scene. Meanwhile, 3D edges have been used for scene representation, particularly man-made built environments, which are dominated by regular planar and linear structures. Indeed, 3D edge detection and matching are commonly exploited either to constrain camera pose estimation, or to generate an abstract representation of the most salient parts of the scene, and even to support mesh reconstruction. In this work, we attempt to jointly use 3D edge extraction and MVS mesh generation to promote edge detail preservation in the final result. Salient 3D edges of the scene are reconstructed with state-of-the-art algorithms and integrated in the dense point cloud to be further used in order to support the mesh triangulation step. Experimental results on benchmark dataset sequences using metric and appearance-based measures are performed in order to evaluate our hypothesis.