Laurent Caraffa, Yanis Marchand, Mathieu Brédif, Bruno Vallet,

Pdf

https://hal.archives-ouvertes.fr/hal-03380593/file/2021216131.pdf

Abstract

We present an out-of-core and distributed surface reconstruction algorithm which scales efficiently on arbitrarily large point clouds (with optical centres) and produces a 3D watertight triangle mesh representing the surface of the underlying scene. Surface reconstruction from a point cloud is a difficult problem and existing state of the art approaches are usually based on complex pipelines making use of global algorithms (i.e. Delaunay triangulation, graph-cut optimisation). For one of these approaches, we investigate the distribution of all the steps (in particular Delaunay triangulation and graph-cut optimisation) in order to propose a fully scalable method. We show that the problem can be tiled and distributed across a cloud or a cluster of PCs by paying a careful attention to the interactions between tiles and using Spark computing framework. We confirm the efficiency of this approach with an in-depth quantitative evaluation and the successful reconstruction of a surface from a very large data set which combines more than 350 million aerial and terrestrial LiDAR points.

Code

A simplified version adapted to the LiDAR HD dataset
https://github.com/lcaraffa/sparkling-wasure

For further updates => Github, Twitter

News

  • 2021-12-10 : Page online

References

@inproceedings{caraffa:hal-03380593,
  TITLE = {Efficiently Distributed Watertight Surface Reconstruction},
  AUTHOR = {CARAFFA, Laurent and Marchand, Yanis and Br{\'e}dif, Mathieu and Vallet, Bruno},
  URL = {https://hal.archives-ouvertes.fr/hal-03380593},
  BOOKTITLE = {2021 International Conference on 3D Vision (3DV)},
  ADDRESS = {London, United Kingdom},
  YEAR = {2021},
  MONTH = Dec,
  PDF = {https://hal.archives-ouvertes.fr/hal-03380593/file/2021216131.pdf},
  HAL_ID = {hal-03380593},
  HAL_VERSION = {v1},
}
@inproceedings{caraffa:hal-02535021,
  TITLE = {Tile \\& Merge: Distributed Delaunay Triangulations for Cloud Computing},
  AUTHOR = {CARAFFA, Laurent and Memari, Pooran and Yirci, Murat and Br{\'e}dif, Mathieu},
  URL = {https://hal.archives-ouvertes.fr/hal-02535021},
  BOOKTITLE = {IEEE Big Data 2019},
  ADDRESS = {Los Angeles, United States},
  YEAR = {2019},
  MONTH = Dec,
  DOI = {10.1109/BigData47090.2019.9006534},
  KEYWORDS = {Computational Geometry ; Delaunay ; Cloud computing ; Spark},
  PDF = {https://hal.archives-ouvertes.fr/hal-02535021/file/Out_of_Core_DT_Short_paper_Camera_Ready.pdf},
  HAL_ID = {hal-02535021},
  HAL_VERSION = {v1},
}