Combined Spectral Descriptors and LMNN for non-rigid 3D shape retrieval
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Latest commit 0464f2d Dec 21, 2016 @johny-c johny-c small typo

README.md

csd_lmnn

csd_lmnn is a package used to learn optimal shape descriptors for 3D non-rigid shapes using well known point descriptors like the Heat Kernel Signature and the Wave Kernel Signature and metric learning, in particular the Large Margin Nearest Neighbor algorithm. It can be used for shape classification, shape similarity ranking and shape retrieval. This repository is an official release of this paper.

Usage

Installation

The code was developed in Matlab 2014b under Ubuntu 14.04. You can clone the repo with:

git clone https://github.com/tum-vision/csd_lmnn.git

Data preparation

Dataset

First download your favourite 3D shapes dataset. I have mainly experimented with the SHREC'14 dataset found at:

http://www.cs.cf.ac.uk/shaperetrieval/download.php

Depending on the resolution of your shapes you might want to downsample them. Meshlab (https://github.com/cnr-isti-vclab/meshlab) can be used for this purpose. A bash script downsample_meshes.sh that uses meshlabserver is provided in the scripts directory to automate this process. A default parameter script QECD_20k.mlx to downsample at 20.000 faces is also included.

Usage of downsampling script

./scripts/downsample_meshes.sh -i path/to/input/shapes/dir -o path/to/output/shapes/dir [-s path/to/meshlab_script]

Setting your paths

You have to customize the file ./code/toplevel_func/init_dataset.m to set the paths to your data directories correctly.

Building mex files

The code for computing the Laplace Beltrami operator is from MeshLP and is relatively old. You might need to adjust your mex compiler settings to get it working for your platform. A script ./code/thirdparty/calc_LB/buildmex.m is included that worked for me along with the resulted executable.

Publication

If you use this code in your work, please cite the following paper.

Ioannis Chiotellis, Rudolph Triebel, Thomas Windheuser and Daniel Cremers, "Non-Rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding", in proceedings of the 14th European Conference on Computer Vision, 2016. (pdf)

@InProceedings{chiotellis2016csdlmnn,
  author = "I. Chiotellis and R. Triebel and T. Windheuser and D. Cremers",
  title = "Non-Rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding",
  booktitle = eccv,
  year = "2016",
  month = "October",
  keywords={shape retrieval, shape representation, supervised learning},
  note = {{<a href="https://github.com/tum-vision/csd_lmnn" target="_blank">[code]</a>} },
}

License and Contact

This work is released under the GNU General Public License Version 3 (GPLv3). Nevertheless it relies on other projects (see directory ./code/thirdparty) which are released under their own licences.

Contact John Chiotellis ✉️ for questions, comments and reporting bugs.