The official code of IterDepth: Iterative Residual Refinement for Outdoor Self-Supervised Multi-Frame Monocular Depth Estimation

Cheng Feng, Zhen Chen, Congxuan Zhang, Weiming Hu, Bing Li, and Feng Lu. – TCSVT 2024

[Link to paper]

We introduce IterDepth, an iterative residual refinement network to dense depth estimation.

If you find our work useful or interesting, please cite our paper:

@article{DBLP:journals/tcsv/FengCZHLL24,
  author       = {Cheng Feng and
                  Zhen Chen and
                  Congxuan Zhang and
                  Weiming Hu and
                  Bing Li and
                  Feng Lu},
  title        = {IterDepth: Iterative Residual Refinement for Outdoor Self-Supervised
                  Multi-Frame Monocular Depth Estimation},
  journal      = {{IEEE} Trans. Circuits Syst. Video Technol.},
  volume       = {34},
  number       = {1},
  pages        = {329--341},
  year         = {2024},
  url          = {https://doi.org/10.1109/TCSVT.2023.3284479},
  doi          = {10.1109/TCSVT.2023.3284479},
  timestamp    = {Fri, 26 Jan 2024 07:57:10 +0100},
  biburl       = {https://dblp.org/rec/journals/tcsv/FengCZHLL24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

To recreate the results from our paper, run:

CUDA_VISIBLE_DEVICES=<your_desired_GPU> \
python train \
    --data_path <your_KITTI_path> \
    --log_dir <your_save_path>  \
    --model_name <your_model_name>
    --iters 6
    --png

Depending on the size of your GPU, you may need to set --batch_size to be lower than 8. Additionally you can train a high resolution model by adding --height 320 --width 1024.

For instructions on downloading the KITTI dataset, see Monodepth2

You can download weights for some pretrained models here:

• KITTI MR (640x192)
• KITTI HR (1024x320)

To evaluate a model on KITTI, run:

CUDA_VISIBLE_DEVICES=<your_desired_GPU> \
python evaluate_depth \
    --data_path <your_KITTI_path> \
    --load_weights_folder <your_model_path>
    --iters 6
    --eval_mono
    --png

If you want to evaluate a teacher network (i.e. the monocular network used for consistency loss), then add the flag --eval_teacher. This will load the weights of mono_encoder.pth and mono_depth.pth, which are provided for our KITTI models.

In my experience, employing different software environments can yield varying evaluation results even when using the same weight file. The specific versions of the software utilized in this article are outlined below:

numpy                         1.21.5
opencv-python                 4.5.5.64
Pillow-SIMD                   9.0.0.post1
torch                         1.11.0
torchvision                   0.12.0

The majority of the code for this project comes from Manydepth. We appreciate the outstanding contributions Project has made to this field.

Meanwhile, the licensing of this project is the same as that of Manydepth.