MVCNN for Multi-Layered Height-maps

This repo contains the training and testing code for classification using a variation of Multi-view CNN (that uses non comutative merge operation) and Multi-Layered Height-map features of 3D shapes. The details are available in the following paper which is to be presented at ECCV 2018:

Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
Kripasindhu Sarkar, Basavaraj Hampiholi, Kiran Varanasi, Didier Stricker	
Computer Vision -- ECCV 2018 European Conference on Computer Vision (ECCV-2018), September 8-14, Munich, Germany	

Bibtex -

@InProceedings{Sarkar2018b,
 Title                    = {Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks},
 Author                   = {Kripasindhu Sarkar and Basavaraj Hampiholi and Kiran Varanasi and Didier Stricker},
 Booktitle                = {Computer Vision -- ECCV 2018},
 Year                     = {2018},
 Publisher                = {Springer International Publishing},
}

Please find the Arxiv preprint of the paper here.

Getting started

1. Clone this repository (lets say to MLH_MVCNN_ROOT).
2. Download the MLH features of ModelNet40 here and extract it to <mlh_root_path>.
3. Edit the train_data_root variable in config.py to point to <mlh_root_path>.

Training

Simply run python train.py to train Multi-View CNN with non-commutative merge operation (for details see the paper) with MLH descriptors. Edit the training parameters in config.py to further control the training. Training for 20 epoches should give a validation accuracy of around 93.1.

Testing

• Run python test.py <path_to_saved_model> to test your trained model. By default, the best model gets saved to MLH_MVCNN_ROOT/data/vgg16_bn_best_model.pth.tar (configurable through config.py) while training.
• We are also providing the trained model used in our paper here. Just download it and use it as <path_to_saved_model> to get the testing results on ModelNet40.