Testing code release for

R. Girdhar, D. F. Fouhey, M. Rodriguez and A. Gupta
Learning a Predictable and Generative Vector Representation for Objects
In Proc. of European Conference on Computer Vision (ECCV), 2016

@inproceedings{Girdhar16b,
    title = {Learning a Predictable and Generative Vector Representation for Objects},
    author = {Girdhar, R. and Fouhey, D.F. and Rodriguez, M. and Gupta, A.},
    booktitle = {ECCV},
    year = {2016},
}

Pre-requisites

1. Caffe (trained and tested with 97f4536, though should work with the latest version). Clone and install in libs dir.
2. Python libs h5py, matplotlib, mayavi.

Download pre-trained models

Download all the models from here to models/ dir.

Testing using the precomputed networks

$ python src/testing/reconst.py  # stores the prediction in output/ folder

Data

The data was stored in HDF5 format for training. The total size of this set is quite large (around 0.5TB), which is hard to release, so I am sharing a subset of the data here.

The data can be accessed as follows (in python):

>>> import h5py
>>> f = h5py.File('batch_0.h5')
>>> images = f['data'].value; print(images.shape)
(198, 3, 227, 227)
>>> voxels = f['label-voxel'].value; voxels.shape
(198, 1, 20, 20, 20)