Remote Sensing (Jan 2022)

LIMOFilling: Local Information Guide Hole-Filling and Sharp Feature Recovery for Manifold Meshes

  • Guohua Gou,
  • Haigang Sui,
  • Dajun Li,
  • Zhe Peng,
  • Bingxuan Guo,
  • Wei Yang,
  • Duo Huang

DOI
https://doi.org/10.3390/rs14020289
Journal volume & issue
Vol. 14, no. 2
p. 289

Abstract

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Manifold mesh, a triangular network for representing 3D objects, is widely used to reconstruct accurate 3D models of objects structure. The complexity of these objects and self-occlusion, however, can cause cameras to miss some areas, creating holes in the model. The existing hole-filling methods do not have the ability to detect holes at the model boundaries, leaving overlaps between the newly generated triangles, and also lack the ability to recover missing sharp features in the hole-region. To solve these problems, LIMOFilling, a new method for filling holes in 3D manifold meshes was proposed, and recovering the sharp features. The proposed method, detects the boundary holes robustly by constructing local overlap judgments, and provides the possibility for sharp features recovery using local structure information, as well as reduces the cost of maintaining manifold meshes thus enhancing their utility. The novel method against the existing methods have been tested on different types of holes in four scenes. Experimental results demonstrate the visual effect of the proposed method and the quality of the generated meshes, relative to the existing methods. The proposed hole-detection algorithm found almost all of the holes in different scenes and qualitatively, the subsequent repairs are difficult to see with the naked eye.

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