OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation
Network Architecture
SOTA on 4D Light Field Benchmark
- Our method achieve competitive performance on the HCI 4D LF Benchmark in terms of all the five accuracy
metrics (i.e., BadPix0.01, BadPix0.03, BadPix0.07, MSE and Q25).
Environment
Ubuntu 16.04
Python 3.8.10
Tensorflow-gpu 2.5.0
CUDA 11.2
Train OccCasNet
- Download HCI Light field dataset from http://hci-lightfield.iwr.uni-heidelberg.de/.
- Unzip the LF dataset and move 'additional/, training/, test/, stratified/ ' into the 'hci_dataset/'.
- Stage 1: Run
python train_occcas.py
- Checkpoint files will be saved in 'LF_checkpoints/XXX_ckp/iterXXXX_valmseXXXX_bpXXX.hdf5'.
- Training process will be saved in
- 'LF_output/XXX_ckp/train_iterXXXXX.jpg'
- 'LF_output/XXX_ckp/val_iterXXXXX.jpg'.
Evaluate OccCasNet
- Run
python evaluation_occcas.py
path_weight='LF_checkpoint/SubFocal_sub_0.5_js_0.1_ckp/iter0010_valmse0.768_bp1.93.hdf5'
Submit OccCasNet
- Run
python submission_occcas.py
path_weight='LF_checkpoint/SubFocal_sub_0.5_js_0.1_ckp/iter0010_valmse0.768_bp1.93.hdf5'
Last modified data: 2023/05/28.
The code is modified and heavily borrowed from LFattNet: https://github.com/LIAGM/LFattNet, SubFocal: https://github.com/chaowentao/SubFocal
The code they provided is greatly appreciated.