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DeepSFM

This is a PyTorch implementation of the ECCV2020 (Oral) paper "DeepSFM: Structure From Motion Via Deep Bundle Adjustment".

In this work, we design a physical driven architecture, namely DeepSFM, inspired by traditional Bundle Adjustment (BA), which consists of two cost volume based architectures for depth and pose estimation respectively, iteratively running to improve both. The explicit constraints on both depth (structure) and pose (motion), when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology. Our framework receives frames of a scene from different viewpoints, and produces depth maps and camera poses for all frames.

Please check the paper and the project webpage for more details.

If you have any question, please contact Xingkui Wei xkwei19@fudan.edu.cn.

Citation

If you use this code for any purpose, please consider citing:

@inProceedings{wei2020deepsfm,
  title={DeepSFM: Structure From Motion Via Deep Bundle Adjustment},
  author={Xingkui Wei and Yinda Zhang and Zhuwen Li and Yanwei Fu and Xiangyang Xue},
  booktitle={ECCV},
  year={2020}
}

Requirements

Building and using requires the following libraries and programs

Pytorch 0.4.0
CUDA 9.0
python 3.6.4
scipy
argparse
tensorboardX
progressbar2
path.py
transforms3d
minieigen

The versions match the configuration we have tested on an ubuntu 16.04 system.

Data Preparation

Training data preparation requires the following libraries and programs

opencv
imageio
joblib
h5py
lz4
  1. Download DeMoN data (https://github.com/lmb-freiburg/demon)
  2. Convert data

[Training data]

# download training data
bash download_traindata.sh
# convert training data into training sequences
python ./dataset/preparation/preparedata_train.py

[Test data]

# download testing data
bash download_testdata.sh
# convert testing data into testing sequences
python ./dataset/preparation/preparedata_test.py

[DeMoN Initialization]

The network assume initial depth maps and camera poses are given. The initialization is not necessary to be accurate. In this implementation the initialization is obtained from "DeMoN: Depth and Motion Network".

Please refer DeMoN(https://github.com/lmb-freiburg/demon) for the details of initial pose and depth map generation.

The final input file structure is shown as follows:

dataset/train
│   train.txt
│   val.txt    
│
└───scene0
│   │   0000.jpg # input images of different views
│   │   0001.jpg
│   │    ...
│   │   0000.npy # ground truth depth maps of different views
│   │   0001.npy  
│   │    ...
│   │   0000_demon.npy # DeMoN predicted depth maps(scaled) of different views
│   │   0001_demon.npy  
│   │    ...
│   │   cam.txt # camera intrinsics
│   │   poses.txt # camera pose of each view
│   │   demon_poses.txt # DeMoN predicted camera pose(scaled) of each view
└───scene1
    ...

dataset/test
│   test.txt
│
└───test_scene0
│   │   0000.jpg
│   │   0001.jpg
│   │    ...
│   │   0000.npy
│   │   0001.npy  
│   │    ...
│   │   0000_demon.npy 
│   │   0001_demon.npy  
│   │    ...
│   │   cam.txt
│   │   poses.txt
│   │   demon_poses.txt
└───test_scene1
    ...

Train

The released code implements the depth map prediction subnet and the camera pose prediction subnet independently.

Depth

The training process and the implementation of the depth subnet is similar to DPSNet. The only difference is that the local geometric consistency constraints is introduced by a additional initial depth maps warping.

# Train the depth subnet. For values of mindepth, nlabel and other parameters, please refer to DPSNet(https://github.com/sunghoonim/DPSNet)
python train.py ./dataset/train/ --mindepth 0.5 --nlabel 64 --pose_init demon --depth_init demon

Pose

The architecture and implementation of the pose subnet is similar to depth subnet.

# Train the pose subnet. 
# [-std_tr, std_tr] and [-std_rot, std_rot] are the sampling range of pose cost volume
python pose_train.py ./dataset/train/ --std_tr 0.27 --std_rot 0.12 --nlabel 10 --pose_init demon --depth_init demon

Test

Depth

# Test the depth subnet. For values of sequence-length, pretrained-dps and other parameters, please refer to DPSNet(https://github.com/sunghoonim/DPSNet)
python test.py ./dataset/test/ --sequence-length 2  --pretrained-dps depth.pth.tar --pose_init demon --depth_init demon --save I0

Pose

# Test the pose subnet. For values of sequence-length, pretrained-dps and other parameters, please refer to DPSNet(https://github.com/sunghoonim/DPSNet)
python pose_test.py ./dataset/test/ --sequence-length 2  --pretrained-dps pose.pth.tar --pose_init demon --depth_init demon --save I0

To run iteratively, change the value of pose_init, depth_init and save.

python test.py ./dataset/test/ --mindepth 0.5 --nlabel 64 --pose_init demon --depth_init demon --save I0
python pose_test.py ./dataset/test/ --std_tr 0.27 --std_rot 0.12 --nlabel 10 --pose_init demon --depth_init demon --save I0
python test.py ./dataset/test/ --mindepth 0.5 --nlabel 64 --pose_init I0 --depth_init I0 --save I1
python pose_test.py ./dataset/test/ --std_tr 0.27 --std_rot 0.12 --nlabel 10 --pose_init I0 --depth_init I0 --save I1
python test.py ./dataset/test/ --mindepth 0.5 --nlabel 64 --pose_init I1 --depth_init I1 --save I2
python pose_test.py ./dataset/test/ --std_tr 0.27 --std_rot 0.12 --nlabel 10 --pose_init I1 --depth_init I1 --save I2
...

The pretrained models can be downloaded at Google drive.

Acknowledgments

The implementation codes borrows heavily from DPSNet. Thanks for the sharing.