Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach

@article{Potamias2021RevisitingPC,
  title={Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach},
  author={Rolandos Alexandros Potamias and Giorgos Bouritsas and Stefanos Zafeiriou},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.14982}
}
. The recent advances in 3D sensing technology have made possible the capture of point clouds in significantly high resolution. How-ever, increased detail usually comes at the expense of high storage, as well as computational costs in terms of processing and visualization op-erations. Mesh and Point Cloud simplification methods aim to reduce the complexity of 3D models while retaining visual quality and relevant salient features. Traditional simplification techniques usually rely on solving a… 
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3 Citations
Background Citations
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Methods Citations
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3 Citations

Neural Mesh Simplification

This work proposes a fast and scalable method that simplifies a given mesh in one-pass, fast, lightweight and differentiable properties that makes it possible to be plugged in every learnable pipeline without introducing a significant overhead.

GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation

The proposed method provides efficient approximations of the shortest paths and geodesic distances estimations and can be directly plugged into any learnable pipeline as well as customized under any differentiable constraint.

Supplementary Neural Mesh Simplification

The Point Sampler is constructed using three stacked DevConv layers, followed with a ReLU activation, and 64 is selected as a hidden dimension, since the aim for a lightweight and fast model.

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