/REDNet-pytorch

the pytorch implementation of "A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset” (CVPR 2020)

Primary LanguagePython

A Pytorch Implementation of REDNet

“A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset” (CVPR 2020)

The proposed network was trained and tested on a single NVIDIA TITAN RTX 2080Ti (24G).

This project is based on the implementation of MVSNet-pytorch. Thank the author for providing the source code (https://github.com/xy-guo/MVSNet_pytorch)

How to Use

Environment

  • python 3.7 (Anaconda)
  • pytorch 1.1.0

Data Preparation

  1. Download the WHU MVS dataset. http://gpcv.whu.edu.cn/data/WHU_dataset/WHU_MVS_dataset.zip.
    (The link in baidu disk: https://pan.baidu.com/s/1aGHFfW26Q8T4oCVa2tPZYQ code：91ae)
  2. Unzip the dataset to the WHU_MVS_dataset folder.

Train

  1. In train.py, set mode to train, set model to rednet
  2. In train.py, set trainpath to your train data path YOUR_PATH/WHU_MVS_dataset/train, set testpath to your train data path YOUR_PATH/WHU_MVS_dataset/test
  3. Train REDNet (TITAN RTX 2080Ti 24G):
python train.py

Test

  1. In train.py, set testpath to your train data path YOUR_PATH/WHU_MVS_dataset/test, set loadckpt to your model path ./checkpoints/whu_rednet/model_000005.ckpt, set depth sample number numdepth.
  2. Run REDNet：
python train.py

The test outputs are stored in YOUR_PATH/WHU_MVS_dataset/test/depths_rednet/, including depth map XXX_init.pfm, probability map XXX_prob.pfm, scaled images XXX.jpg and camera parameters XXX.txt.