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Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation

Python 99.06% Dockerfile 0.94%

subfocal's Introduction

Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation

Paper link

Network Architecture

Network Architecture

SOTA on 4D Light Field Benchmark

  • Our method ranks the first place 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).

  • For more detail comparison, please use the link below.
  • Benchmark link

Environment

Ubuntu            16.04
Python            3.8.10
Tensorflow-gpu    2.5.0
CUDA              11.2

Two-Stage Train SubFocal

  1. Download HCI Light field dataset from http://hci-lightfield.iwr.uni-heidelberg.de/.
  2. Unzip the LF dataset and move 'additional/, training/, test/, stratified/ ' into the 'hci_dataset/'.
  3. Stage 1: Run python train_sub.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'.
  1. Stage 2: Run python train_sub_js.py
  • Satge 1 model as pretrained, finetune load_weight_is=True
  • path_weight='LF_checkpoint/SubFocal_sub_0.5_ckp/iter0049_valmse0.845_bp2.04.hdf5'

Evaluate SubFocal

  • Run python evaluation.py
    • path_weight='LF_checkpoint/SubFocal_sub_0.5_js_0.1_ckp/iter0010_valmse0.768_bp1.93.hdf5'

Submit SubFocal

  • Run python submission.py
    • path_weight='LF_checkpoint/SubFocal_sub_0.5_js_0.1_ckp/iter0010_valmse0.768_bp1.93.hdf5'

Last modified data: 2022/08/18.

The code is modified and heavily borrowed from LFattNet: https://github.com/LIAGM/LFattNet

The code they provided is greatly appreciated.

subfocal's People

Contributors

zizieada avatar chaowentao avatar

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