Skip to content

Latest commit

 

History

History
89 lines (75 loc) · 3.91 KB

README.md

File metadata and controls

89 lines (75 loc) · 3.91 KB
Raw

DDR-Net

The official implementation of "DDR-Net: Learning Multi-Stage Multi-View Stereo With Dynamic Depth Range"(https://arxiv.org/abs/2103.14275) arc

Installation

Requirements

  • python 3.6
  • Pytorch >= 1.0.0 and <= 1.2.0 (We advise not to use Pytorch version higher than 1.2.0 because there may be some problems on interpolation.)
  • CUDA >= 9.0
pip install -r requirements.txt

Option for speeding up training: Apex

install apex to enable synchronized batch normalization

git clone https://github.com/NVIDIA/apex.git
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Training and testing on DTU dataset.

Training

├── Cameras    
├── Depths
├── Depths_raw   
├── Rectified
├── Cameras
  • In train.sh, set MVS_TRAINING to where the training dataset is.
  • Train DDR-Net (Multi-GPU training):
export NGPUS=4
export save_results_dir="./checkpoints"  
./train.sh $NGPUS $save_results_dir  --ndepths "48,32,8"  --depth_inter_r "4,2,1"   --dlossw "0.5,1.0,2.0"  --batch_size 2 --eval_freq 3

If apex is installed, you can use sync_bn in training:

./train.sh $NGPUS $save_results_dir  --ndepths "48,32,8"  --depth_inter_r "4,2,1"   --dlossw "0.5,1.0,2.0"  --batch_size 2 --eval_freq 3  --using_apex  --sync_bn

Testing and Fusion

  • Download the preprocessed test data DTU testing data (from Original MVSNet) and unzip it, which should contain one cams folder, one images folder and one pair.txt file.
  • In test_dtu.sh, set TESTPATH to where the test dataset is.
  • Set CKPT_FILE as your checkpoint file, you also can utilize our pretrained model (checkpoints/DDR-Net.ckpt).
  • Test DDR-Net and Fusion:
  • The method of fusion is optional, including normal fusion and Gipuma fusion.
  • We suggest Gipuma to fusion to reproduce our results (fusibile is required to install). the script is borrowed from MVSNet. Note that more than 20 GB memory is needed for tanks and temples dataset fusion.
git clone https://github.com/YoYo000/fusibile
cd fusibile
mkdir build&&cd build
cmake..
make
  • After installing fusibile, set --fusibile_exe_path to the place where you install fusibile.
  • Set root_path as the path of depth map output and target_path as the path of point cloud reconstruction.
./test_dtu.sh

./test_tanks.sh %%test on tanks and temples and fusion
  • Point cloud reconstruction can be evaluated by offical Matlab code in DTU

Results on DTU

Acc. Comp. Overall.
DDR-Net(D=48,32,8) 0.339 0.320 0.329

Results on Tanks and Temples benchmark (D=48,32,8)

Mean Family Francis Horse Lighthouse M60 Panther Playground Train
54.91 76.18 53.36 43.43 55.20 55.57 52.28 56.04 47.17

Please refer to leaderboard.

if you have any issues, please make an issue or send an email to us ([email protected]). We will reply as soon as possible.