Confidence driven image fusion based on TGV regularization
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cuda
data
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LICENSE.md
README.md
cfusion.m
compute_errors.m
demo_kitti.m
derivative5.m
disp_int.m
disp_to_color.m
dm_fusion_prepare.m
dmap2normap.m
evaluate_error_kitti.m
g_u.m
get_fusion_options.m
heatmap_overlay.m
kernel_conf.m
proc_synth_data_objects.m
proc_synth_data_urban.m
randlap.m
reproject_dm_rel.m
showSurf.m

README.md

Confidence driven TGV fusion

This repository contains the source code corresponding to the method presented in the paper:

  • Confidence driven TGV fusion, V. Ntouskos, F. Pirri, arXiv: arXiv:1603.09302, 2016.

###Contact:

Valsamis Ntouskos ntouskos@diag.uniroma1.it, http://www.diag.uniroma1.it/~ntouskos;

ALCOR Lab alcor@diag.uniroma1.it http://www.diag.uniroma1.it/~alcor,

KITTI 2012##

###Instructions###

To run the demo you need to download the KITTI 2012 multiview dataset (data, calibration files, and multi-view extension) from here.

The disparity maps for all the stereo-pairs of the dataset, computed based on the method presented in the paper "Confidence driven TGV fusion", J. Zbontar and Y. LeCun, CVPR, 2015, are available for download from here.

###Quickstart###

  1. Compile the CUDA kernels located in the ./cuda folder. Under Windows you can use the provided Makefile: nmake -f Makefile.win all;

  2. Modify the path variables basedir and w_path in the demo script demo_kitti.

  3. Execute the demo script: demo_kitti.

##Synthetic Datasets ###Instructions### You can run the scripts that process the synthetic datasets used in the paper. These datasets are available for download from here.

###Quickstart###

  1. Compile the CUDA kernels located in the ./cuda folder. Under Windows you can use the provided Makefile: nmake -f Makefile.win all;

  2. Modify the path variables basedir and w_path in the demo scripts proc_synth_data_objects and proc_synth_data_urban.

  3. Execute the script: proc_synth_data_objects or proc_synth_data_urban.

  4. Use script showSurf to visualize the surfaces corresponding to the fused depth images.

External dependencies

You need the CUDA Toolkit (version 6 or later) and a compatible compiler in order to compile the CUDA kernels.