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Laurent Caraffa, Yanis Marchand, Mathieu Brédif, Bruno Vallet, {: style="color:gray; font-size: 120%; text-align: center;"}

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

{% highlight bibtex %} @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}, } {% endhighlight %}

{% highlight bibtex %} @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}, } {% endhighlight %} {% include text-expand.html %}