gaochangwu / lfasr-fs-gaf

Repository for "Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion", IEEE TPAMI 2020

LFASR-FS-GAF

PyTorch implementation of IEEE TPAMI 2020 paper: "Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion".

[Paper]

Requirements

• Python 3.6
• PyTorch 1.3
• Matlab (for training/test data generation)

Dataset

We provide MATLAB code for preparing the training and test data. Please first download light field datasets, and put them into corresponding folders in LFData.

Demo

To reproduce the experimental results presented in the paper, run:

(Ours (fixed) under task 2x2→7x7 for synthetic LF data)

python test_pretrained.py --model_dir pretrained_models --save_dir results --arb_sample 0 --angular_out 7 --angular_in 2 --train_dataset HCI --test_dataset HCI --test_path ./LFData/test_HCI.h5 --psv_range 4 --psv_step 50 --input_ind 0 6 42 48 --save_img 1 --crop 1

(Ours (fixed) under task 2x2→7x7 for Lytro LF data)

python test_pretrained.py --model_dir pretrained_models --save_dir results --arb_sample 0 --angular_out 7 --angular_in 2 --train_dataset SIG --test_dataset 30scenes --test_path ./LFData/test_30scenes.h5 --psv_range 2 --psv_step 50 --input_ind 0 6 42 48 --save_img 1 --crop 1

(Ours (flexible) under task 4→7x7 for synthetic LF data)

python test_pretrained.py --model_dir pretrained_models --save_dir results --arb_sample 1 --angular_out 7 --angular_in 4 --train_dataset HCI --test_dataset HCI --test_path ./LFData/test_HCI.h5 --psv_range 4 --psv_step 50 --input_ind 16 18 30 32 --save_img 1 --crop 1

(Ours (flexible) under task 4→7x7 for Lytro LF data)

python test_pretrained.py --model_dir pretrained_models --save_dir results --arb_sample 1 --angular_out 7 --angular_in 4 --train_dataset SIG --test_dataset 30scenes --test_path ./LFData/test_30scenes.h5 --psv_range 2 --psv_step 50 --input_ind 11 15 33 37 --save_img 1 --crop 1

Training

To re-train the model, run:

(Ours (fixed) under task 2x2→7x7 for synthetic LF data)

python train.py --arb_sample 0 --angular_in 2 --angular_out 7 --dataset HCI --dataset_path ./LFData/train_HCI.h5 --psv_range 4 --psv_step 50 --patch_size 112 --num_cp 100 --lr 6e-5

(Ours (fixed) under task 2x2→7x7 for Lytro LF data)

python train.py --arb_sample 0 --angular_in 2 --angular_out 7 --dataset SIG --dataset_path ./LFData/train_SIG.h5 --psv_range 2 --psv_step 50 --patch_size 64 --num_cp 20 --lr 1e-4

(Ours (flexible) under task 4→7x7 for synthetic LF data)

python train.py --arb_sample 1 --angular_in 4 --angular_out 7 --dataset HCI --dataset_path ./LFData/train_HCI.h5 --psv_range 4 --psv_step 50 --patch_size 64 --num_cp 100 --lr 1e-4

(Ours (flexible) under task 4→7x7 for Lytro LF data)

python train.py --arb_sample 1 --angular_in 4 --angular_out 7 --dataset SIG --dataset_path ./LFData/train_SIG.h5 --psv_range 2 --psv_step 50 --patch_size 64 --num_cp 20 --lr 1e-4