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Sparse Iterative Closest Point Implementation

As part of a work for the "Point Cloud and 3D modelization" from the IASD/MVA course at Les Mines.

This repository contains an implementation in Python and an analysis report of the Sparse Iterative Closest Point (SICP) algorithm, as introduced in the paper:

[Sparse Iterative Closest Point] 
Sofien Bouaziz, Andrea Tagliasacchi, Mark Pauly  
Symposium on Geometry Processing 2013, Computer Graphics Forum

Features

  • Implementation of ICP for point-to-point and point-to-plane correspondences.
  • Implementation of re-weight ICP for point-to-point correspondences.
  • Implementation of ICP with correspondences pruning for point-to-point correspondences.
  • Implementation of the Sparse ICP algorithm for point-to-point and point-to-plane correspondences.
  • Examples with popular 3D scan datasets (e.g., bunny and owl models).
  • Utilities for data preprocessing.
  • Visualization tools to inspect the alignment results and convergence behavior.

Running a test :

To run the SICP algorithm on a specific dataset, use the main.py script with appropriate arguments. For example, to run the SICP algorithm on the owl dataset with parameter p=1.0 and 30 iterations:

python main.py --test owls_plane --p 1 --ite 30