Skip to content

hazirbas/poselstm-pytorch

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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

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
Dataset beta PoseNet (CAFFE) PoseNet PoseLSTM (TF) PoseLSTM
King's College 500 1.92m 5.40° 1.19m 4.51° 0.99m 3.65° 0.90m 3.96°
Old Hospital 1500 2.31m 5.38° 1.91m 4.05° 1.51m 4.29° 1.79m 4.28°
Shop Façade 100 1.46m 8.08° 1.30m 8.13° 1.18m 7.44° 0.98m 6.20°
St Mary's Church 250 2.65m 8.48° 1.89m 7.27° 1.52m 6.68° 1.68m 6.41°

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.

About

PyTorch implementation of PoseLSTM and PoseNet

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages