Published October 20, 2022 | Version v1
Dataset Open

PCEDNet - Replicability material

Creators

  • 1. IRIT, Université de Toulouse, CNRS
  • 2. CNRS, IRIT, Université de Toulouse

Description

Material required to replicate the paper:

Chems-Eddine Himeur, Thibault Lejemble, Thomas Pellegrini, Mathias Paulin, Loic Barthe, and Nicolas Mellado. 2021. 
PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds. 
ACM Trans. Graph. 41, 1, Article 10 (February 2022), 21 pages. 
DOI:https://doi.org/10.1145/3481804

Includes the following files:

  • networks.zip: pre-trained networks,
  • default.zip: dataset provided by the authors, including Ground Truth labels (see paper for more details)
  • abc.zip: dataset generated from the ABC dataset, including Ground Truth labels (see paper for more details)
  • point-clouds.zip: point-clouds without Ground Truth (see paper for more details) 

 

Notes

Website: https://storm-irit.github.io/pcednet-supp/

Files

abc.zip

Files (3.6 GB)

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md5:e7f2dd0254bb6e3e225c185a6ca7f403
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md5:8f9e4986928ef5130ee07c381aac25b9
608.8 kB Preview Download
md5:78068f62c31a9f6edbb36d8ebaf23209
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md5:d8a09d213efcf675057c81fca0dcf517
82.8 MB Preview Download

Additional details

Related works

Compiles
Journal article: 10.1145/3481804 (DOI)
Is derived from
Dataset: https://deep-geometry.github.io/abc-dataset/ (URL)

Funding

ALTA – Analysis of Light Transport operators and Applications ANR-11-BS02-0006
Agence Nationale de la Recherche