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Reimplementation of NeRF (Neural Radiance Fields) (ECCV2020)

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NeRF in Pytorch

Pytorch Re-Implementation of NeRF (Neural Radiance Fields)

Paper : https://arxiv.org/abs/2003.08934

Preparation

Environment

conda env create -f environment.yml
conda activate nerf

Dataset

Download data for two example datasets: lego and fern

bash download_data.sh

Download more dataset from link below https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1

synthetic datasets : [chair, drums, ficus, hotdog, lego, materials, mic, ship] llff datasets : [fern, flower, fortress, horns, leaves, orchids, room, trex]

Data Directory

Blender Dataset

nerf_synthetic
    |-- chair
        |-- train
            |-- r_0.png
            |-- r_1.png
                    ...
            |-- r_99.png
        |-- test
        |-- val
        |-- transforms_train.json
        |-- transforms_test.json
        |-- transforms_val.json
    |-- drums
    ...

LLFF Dataset

nerf_llff_data
    |-- fern
        |-- images
            |-- IMG_0000.JPG
            |-- IMG_0001.JPG
                    ...
        |-- sparse
        |-- database.db
        |-- poses_bounds.npy
            ...
    |-- flower
    ...

Experiments

Training

set 'data_root' path in config file to dataset root

python main.py --config configs/blender/lego.txt
python main.py --config configs/llff/fern.txt

Results

• Official Paper Results (Blender)

data model Batch rays resolution PSNR SSIM LPIPS
chair 200,000 4096 800 x 800 33.00 0.967 0.046
drums 200,000 4096 800 x 800 25.01 0.925 0.091
ficus 200,000 4096 800 x 800 30.13 0.964 0.044
hotdog 200,000 4096 800 x 800 36.18 0.974 0.121
lego 200,000 4096 800 x 800 32.54 0.961 0.050
materials 200,000 4096 800 x 800 29.62 0.949 0.063
mic 200,000 4096 800 x 800 32.91 0.980 0.028
ship 200,000 4096 800 x 800 28.65 0.856 0.206
mean 200,000 4096 800 x 800 31.01 0.947 0.081

• This Repo Results (Blender)

data iter Batch rays resolution PSNR SSIM LPIPS
chair 200,000 4096 800 x 800 33.92 0.966 0.048
drums 200,000 4096 800 x 800 24.96 0.922 0.102
ficus 200,000 4096 800 x 800 29.82 0.960 0.063
hotdog 200,000 4096 800 x 800 36.20 0.974 0.049
lego 200,000 4096 800 x 800 32.02 0.958 0.060
materials 200,000 4096 800 x 800 29.41 0.943 0.076
mic 200,000 4096 800 x 800 33.21 0.981 0.028
ship 200,000 4096 800 x 800 28.47 0.854 0.186
mean 200,000 4096 800 x 800 31.00 0.945 0.077

TEST RESULT of 200 images from Test Dataset

• This Repo Results (LLFF)

data model Batch rays resolution PSNR SSIM LPIPS
fern Coarse+Fine 4096 800 x 800 23.98 0.773 0.224
flower Coarse+Fine 4096 800 x 800 28.73 0.896 0.110
fortress Coarse+Fine 4096 800 x 800 32.02 0.925 0.081
horns Coarse+Fine 4096 800 x 800 30.11 0.921 0.125
leaves Coarse+Fine 4096 800 x 800 22.51 0.815 0.187
orchids Coarse+Fine 4096 800 x 800 20.74 0.734 0.210
room Coarse+Fine 4096 800 x 800 32.78 0.963 0.094
trex Coarse+Fine 4096 800 x 800 26.38 0.924 0.149
mean Coarse+Fine 4096 800 x 800 27.16 0.869 0.148

SSIM code from https://github.com/dingkeyan93/IQA-optimization

LPIPS from https://pypi.org/project/lpips/

Train Environment

ubuntu 20.04
GeForce RTX 3090
cuda : v11.1
cuDNN : v8.0.5

• Render Results (Blender)

Render RGB Render DISP

• Render Results (LLFF)

Render RGB Render DISP

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Reimplementation of NeRF (Neural Radiance Fields) (ECCV2020)

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