Torch Implementation of NIPS'16 paper: Perspective Transformer Nets
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pt_layer
scripts
utils
.gitignore
LICENSE
README.md
demo_pretrain_singleclass.sh
demo_train_cnn_vol_singleclass.sh
demo_train_ptn_comb_singleclass.sh
demo_train_ptn_proj_singleclass.sh
download_models.sh
eval_models.sh
exp_multiclass.txt
exp_singleclass.txt
install_ptnbhwd.sh
prepare_data.sh

README.md

Perspective Transformer Nets (PTN)

This is the code for NIPS 2016 paper Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision by Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo and Honglak Lee

Please follow the instructions to run the code.

Requirements

PTN requires or works with

  • Mac OS X or Linux
  • NVIDIA GPU

Installing Dependency

The following command installs the Perspective Transformer Layer:

./install_ptnbhwd.sh

Dataset Downloading

  • Please run the command to download the pre-processed dataset (including rendered 2D views and 3D volumes):
./prepare_data.sh

Pre-trained Models Downloading (single-class experiment)

PTN-Proj: ptn_proj.t7

PTN-Comb: ptn_comb.t7

CNN-Vol: cnn_vol.t7

  • The following command downloads the pre-trained models:
./download_models.sh

Testing using Pre-trained Models (single-class experiment)

  • The following command evaluates the pre-trained models:
./eval_models.sh

Training (single-class experiment)

  • If you want to pre-train the view-point indepedent image encoder on single-class, please run the following command. Note that the pre-training could take a few days on a single TITAN X GPU.
./demo_pretrain_singleclass.sh
  • If you want to train PTN-Proj (unsupervised) on single-class based on pre-trained encoder, please run the command.
./demo_train_ptn_proj_singleclass.sh
  • If you want to train PTN-Comb (3D supervision) on single-class based on pre-trained encoder, please run the command.
./demo_train_ptn_comb_singleclass.sh
  • If you want to train CNN-Vol (3D supervision) on single-class based on pre-trained encoder, please run the command.
./demo_train_cnn_vol_singleclass.sh

Using your own camera

  • In many cases, you want to implement your own camera matrix (e.g., intrinsic or extrinsic). Please feel free to modify this function.

  • Before start your own implementation, we recommand to go through some basic camera geometry in this computer vision textbook written by Richard Szeliski (see Eq 2.59 at Page 53).

  • Note that in our voxel ray-tracing implementation, we used the inverse camera matrix.

Third-party Implementation

Besides our torch implementation, we recommend to see also the following third-party re-implementation:

  • TensorFlow Implementation: This re-implementation was developed during Xinchen's Google internship; If you find a bug, please file a bug including @xcyan.

Citation

If you find this useful, please cite our work as follows:

@incollection{NIPS2016_6206,
title = {Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision},
author = {Yan, Xinchen and Yang, Jimei and Yumer, Ersin and Guo, Yijie and Lee, Honglak},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett},
pages = {1696--1704},
year = {2016},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/6206-perspective-transformer-nets-learning-single-view-3d-object-reconstruction-without-3d-supervision.pdf}
}