Chris Rockwell, Justin Johnson and David F. Fouhey

Project Website | Paper | Supplemental

We propose three small modifications to a ViT via the Essential Matrix Module, enabling computations similar to the Eight-Point algorithm. The resulting mix of visual and positional features is a good inductive bias for pose estimation.

Anaconda install:

• You'll need to build lietorch using c++; the c++ version needs to be compatable with the version g++ which built Pytorch.
• My setup steps on a GTX 1080 Ti (gcc 8.4.0, CUDA 10.2):
  • conda create -n rel_pose python=3.9
  • conda activate rel_pose; pip3 install opencv-python; pip3 install tqdm; pip3 install PyYAML;
  • pip3 install torch==1.8.1+cu102 -f https://nelsonliu.me/files/pytorch/whl/torch_stable.html; pip3 install torchvision==0.9.1
  • pip3 install git+https://github.com/princeton-vl/lietorch.git
  • pip3 install scipy; pip3 install tensorboard

Download & extract pretrained models replicating paper results:

wget https://fouheylab.eecs.umich.edu/~cnris/rel_pose/modelcheckpoints/pretrained_models.zip --no-check-certificate
unzip pretrained_models.zip

Demo script to predict pose on arbitrary image pair:

python demo.py --img1 demo/matterport_1.png --img2 demo/matterport_2.png --ckpt pretrained_models/matterport.pth
python demo.py --img1 demo/interiornet_t_1.png --img2 demo/interiornet_t_2.png --ckpt pretrained_models/interiornet_t.pth
python demo.py --img1 demo/streetlearn_t_1.png --img2 demo/streetlearn_t_2.png --ckpt pretrained_models/streetlearn_t.pth

Script to generate epipolar lines: (please modify pose / image pair inside script)

python generate_epipolar_imgs.py

Download and setup data following the steps of Jin et al. (Matterport) and Cai et al. (InteriorNet and StreetLearn).

• StreetLearn and InteriorNet require rendering. See here for more details. You'll need to clone their repo for rendering as well as to get dataset metadata.
• Matterport does not require rendering:

wget https://fouheylab.eecs.umich.edu/~jinlinyi/2021/sparsePlanesICCV21/split/mp3d_planercnn_json.zip
wget https://fouheylab.eecs.umich.edu/~jinlinyi/2021/sparsePlanesICCV21/data/rgb.zip
unzip mp3d_planercnn_json.zip; unzip rgb.zip

• You'll need to update MATTERPORT_PATH and INTERIORNET_STREETLEARN_PATH to root directories once datasets are setup.

Evaluation scripts are as follows:

sh scripts/eval_matterport.sh
sh scripts/eval_interiornet.sh
sh scripts/eval_interiornet_t.sh
sh scripts/eval_streetlearn.sh
sh scripts/eval_streetlearn_t.sh

Data setup is the same as in evaluation. Training scripts are as follows:

sh scripts/train_matterport.sh
sh scripts/train_interiornet.sh
sh scripts/train_interiornet_t.sh
sh scripts/train_streetlearn.sh
sh scripts/train_streetlearn_t.sh

To train with small data, simply add the argument --use_mini_dataset to train script; eval is unchanged.

Pretrained models trained on the small InteriorNet and StreetLearn datasets are here.

wget https://fouheylab.eecs.umich.edu/~cnris/rel_pose/modelcheckpoints/pretrained_models_mini_dataset.zip --no-check-certificate
unzip pretrained_models_mini_dataset.zip

• Our Matterport-trained model follows the habitat camera coordinate convention. The circle represents "in towards you"

If you use this code for your research, please consider citing:

@inProceedings{Rockwell2022,
  author = {Chris Rockwell and Justin Johnson and David F. Fouhey},
  title = {The 8-Point Algorithm as an Inductive Bias for Relative Pose Prediction by ViTs},
  booktitle = {3DV},
  year = 2022
}

Thanks to Linyi Jin, Ruojin Cai and Zach Teed for help replicating and building upon their works. Thanks to Mohamed El Banani, Karan Desai and Nilesh Kulkarni for their many helpful suggestions. Thanks to Laura Fink and UM DCO for their tireless support with computing!