PoseLSTM and PoseNet implementation in PyTorch

This is the PyTorch implementation for PoseLSTM and PoseNet, developed based on Pix2Pix code.

Prerequisites

• Linux
• Python 3.5.2
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Install PyTorch and dependencies from http://pytorch.org
• Clone this repo:

git clone https://github.com/hazirbas/posenet-pytorch
cd posenet-pytorch
pip install -r requirements.txt

PoseNet train/test

• Download a Cambridge Landscape dataset (e.g. KingsCollege) under datasets/ folder.
• Compute the mean image

python util/compute_image_mean.py --dataroot datasets/KingsCollege --height 256 --width 455 --save_resized_imgs

• Train a model:

python train.py --model posenet --dataroot ./datasets/KingsCollege --name posenet/KingsCollege/beta500 --beta 500 --gpu 0

• To view training errors and loss plots, set --display_id 1, run python -m visdom.server and click the URL http://localhost:8097. Checkpoints are saved under ./checkpoints/posenet/KingsCollege/beta500/.
• Test the model:

python test.py --model posenet  --dataroot ./datasets/KingsCollege --name posenet/KingsCollege/beta500 --gpu 0

The test errors will be saved to a text file under ./results/posenet/KingsCollege/beta500/.

PoseLSTM train/test

• Train a model:

python train.py --model poselstm --dataroot ./datasets/KingsCollege --name poselstm/KingsCollege/beta500 --beta 500 --niter 1200 --gpu 0

• Test the model:

python test.py --model poselstm --dataroot ./datasets/KingsCollege --name poselstm/KingsCollege/beta500 --gpu 0

Initialize the network with the pretrained googlenet trained on the Places dataset

If you would like to initialize the network with the pretrained weights, download the places-googlenet.pickle file under the pretrained_models/ folder:

wget https://vision.in.tum.de/webarchive/hazirbas/poselstm-pytorch/places-googlenet.pickle

Optimization scheme and loss weights

• We use the training scheme defined in PoseLSTM
• Note that mean subtraction is not used in PoseLSTM models
• Results can be improved with a hyper-parameter search

Citation

@inproceedings{PoseNet15,
  title={PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization},
  author={Alex Kendall, Matthew Grimes and Roberto Cipolla },
  journal={ICCV},
  year={2015}
}
@inproceedings{PoseLSTM17,
  author = {Florian Walch and Caner Hazirbas and Laura Leal-Taixé and Torsten Sattler and Sebastian Hilsenbeck and Daniel Cremers},
  title = {Image-based localization using LSTMs for structured feature correlation},
  month = {October},
  year = {2017},
  booktitle = {ICCV},
  eprint = {1611.07890},
  url = {https://github.com/NavVisResearch/NavVis-Indoor-Dataset},
}

Acknowledgments

Code is inspired by pytorch-CycleGAN-and-pix2pix.