/marching_windows

Primary LanguageC++

Marching Windows: Scalable Mesh Generation for Volumetric Data with Multiple Materials

This repository contains code for the paper: Marching Windows: Scalable Mesh Generation for Volumetric Data with Multiple Materials

inal volumetric data. In this paper we propose a novel approach, called Marching Windows, that uses a moving window and a disk-swap strategy to reduce the run-time memory footprint, devise a new scheme that guarantees to preserve the topological structure of the original dataset, and adopt an error-guided optimization technique to improve both geometric approximation error.

Set Up Environment

Below are the main packages we used in thie project:

  • boost 1.56.0
  • CGAL
  • hdf5 1.10.1
  • opencv 2.4.9
  • SparseSky
  • tetgen
  • zlib

Repository Structure

Below are the main directories in the repository:

  • console/: the code call the main functions of simlification;
  • simplification3d/: the main code for the simplification functions.
  • data/: the code to prepare the datasets from labeled data

Running the Code

Dataset preparation

  • Download the image files
  • Run png2svdata python scripts to process the data

Usage and Options

Compile the code (with preferably VS2012), run the compiled project with command as:

.\simplification3d [data_folder] [target_simplifying ratio_usually_0.01] [odt_count] [target_edge_variance] [dim_x] [dim_y] [dim_z]

Results

Segmentation

Results of different datasets.

Citation

If any part of this code is used, please give appropriate citation to our paper. 

Zhang W, Yue Y, Pan H, Chen Z, Wang C, Pfister H, Wang W. Marching Windows: Scalable Mesh Generation for Volumetric Data with Multiple Materials. IEEE Trans Vis Comput Graph. 2022 Dec 1;PP. doi: 10.1109/TVCG.2022.3225526. Epub ahead of print. PMID: 36455093.

Authors

License

The dataset provided here is for research purposes only. Commercial use is not allowed. The data is held under the following license: Attribution-NonCommercial-ShareAlike 4.0 International