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dataset
dependencies
utils
.gitattributes
QSlim.exe
README.md
autocrop.m
average_im2400.mat
check_face_vertex.m
cnn_initialise.m
cnn_output_fast.m
cnn_shape.m
cnn_shape_get_batch.m
cnn_shape_train_dag1.m
density.m
get_imdb.m
getoptions.m
human.off
mark_visible_vertices.m
perform_mesh_simplification.m
perform_mesh_smoothing.m
ray_trianle_intersect.m
read_smf.m
run_train.m
same_side.m
setup.m
skateboard.off
triangulation2adjacency.m
write_smf.m

README.md

CFSCNN

The source codes are based on matconvnet.

Create a new folder 'data' and then create 3 subfolders 'deploynet', 'modelnet40' and 'models' in it.

Download the pretrained net from

https://drive.google.com/open?id=1yc982kKOCVwr5OROoMCbx_qqGwLe_Wv4

Save the downloaded net file 'net-deployed.mat' in \data\deploynet\

For a demo, simply implement the following lines in MATLAB command window

setup;

sa1 = cnn_output_fast('human.off');

You can also input a scene:

sa2 = cnn_output_fast('skateboard.off');

Note that the program accepts OFF or OBJ files for the input 3D models.

If you want to train it using the modelnet40 dataset, please follow the steps below:

  1. Download the baseline VGG19 net from the official website of matconvnet or

https://drive.google.com/open?id=1jezzXXTUnySn0tTtpt41lP61o8r1c71N

Save it in \data\models\

  1. Download the ModelNet40 dataset and render all of the models using render_views_of_all_meshes_in_a_folder.m and create_data.m in the utils folder. Then save the rendered images (including 40 folders) in \data\modelnet40.

  2. Run run_train.m where the get_imdb function only need to be implemented at the first time of training.

Please cite our paper if you use the codes:

Ran Song, Yonghuai Liu, Paul L. Rosin. Mesh Saliency via Weakly Supervised Classification-for-Saliency CNN. IEEE Transactions on Visualization and Computer Graphics, 13 pages, 2019.

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