cd ./nerf
conda create -n nerf
conda activate dnerf
pip install -r requirements.txt
Download data for three datasets: lego, trex and fern. Place the downloaded dataset according to the following directory structure:
├── data
│ ├── nerf_llff_data
│ │ └── fern
│ │ └── trex
| ├── nerf_synthetic
| | └── lego
To train a low-res lego NeRF:
python run_nerf.py --config configs/lego.txt
To train a low-res trex NeRF:
python run_nerf.py --config configs/trex.txt
To train a low-res fern NeRF:
python run_nerf.py --config configs/fern.txt
To test NeRF trained on different datasets:
python run_nerf.py --config configs/{DATASET}.txt --render_only
replace {DATASET} with lego | trex | fern.
cd ./D-NeRF
conda create -n dnerf python=3.6
conda activate dnerf
pip install -r requirements.txt
cd torchsearchsorted
pip install .
cd ..
Download data for three datasets: lego, trex and standup. Place the downloaded dataset according to the following directory structure:
├── data
│ ├── lego
│ ├── trex
│ ├── standup
To test D-NeRF trained on different datasets:
python run_dnerf.py --config configs/{DATASET}.txt
replace {DATASET} with lego | trex | standup.
To test NeRF trained on different datasets:
python run_dnerf.py --config configs/{DATASET}.txt --render_only --render_test
replace {DATASET} with lego | trex | standup.
Download data for three datasets: lego, curl and shi. Place the downloaded dataset according to the following directory structure:
├── data
│ ├── lego
│ ├── curl
│ ├── shi
The code can be run under any environment with Python 3.8 and above.
conda create --name nerfies python=3.8
Next, install the required packages:
pip install -r requirements.txt
Install the appropriate JAX distribution for your environment by following the instructions here. For example:
# For CUDA version 11.0
pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
After preparing a dataset, you can train a Nerfies by running:
export DATASET_PATH=/path/to/dataset
export EXPERIMENT_PATH=/path/to/save/experiment/to
CUDA_VISIBLE_DEVICES=1 python train_1stage.py \
--data_dir $DATASET_PATH \
--base_folder $EXPERIMENT_PATH \
--gin_configs configs/ourdata_lego2.txt
After preparing a dataset, you can Freeze training a Nerfies steps by running:
export DATASET_PATH=/path/to/dataset
export EXPERIMENT_PATH=/path/to/save/experiment/to
CUDA_VISIBLE_DEVICES=4 python train_2stage.py \
--base_folder $EXPERIMENT_PATH \
--gin_bindings="data_dir='$DATASET_PATH'" \
----gin_configs configs/ourdata_lego2.txt
To plot telemetry to Tensorboard and render checkpoints on the fly, also launch an evaluation job by running:
CUDA_VISIBLE_DEVICES=1 python eval.py \
--data_dir $DATASET_PATH \
--base_folder $EXPERIMENT_PATH \
--gin_configs configs/test_vrig.gin
Download data for three datasets: lego, curl and shi. Place the downloaded dataset according to the following directory structure:
├── data
│ ├── lego
│ ├── curl
│ ├── shi
The code can be run under any environment with Python 3.8 and above.
conda create --name hypernerf python=3.8
Next, install the required packages:
pip install -r requirements.txt
Install the appropriate JAX distribution for your environment by following the instructions here. For example:
# For CUDA version 11.1
pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
After preparing a dataset, you can train a Hypernerf by running:
export DATASET_PATH=/path/to/dataset
export EXPERIMENT_PATH=/path/to/save/experiment/to
CUDA_VISIBLE_DEVICES=4 python train_1stage.py \
--base_folder $EXPERIMENT_PATH \
--gin_bindings="data_dir='$DATASET_PATH'" \
----gin_configs configs/ourdata_lego2.txt
After preparing a dataset, you can Freeze training a Hypernerf steps by running:
export DATASET_PATH=/path/to/dataset
export EXPERIMENT_PATH=/path/to/save/experiment/to
CUDA_VISIBLE_DEVICES=4 python train_2stage.py \
--base_folder $EXPERIMENT_PATH \
--gin_bindings="data_dir='$DATASET_PATH'" \
----gin_configs configs/ourdata_lego2.txt
To plot telemetry to Tensorboard and render checkpoints on the fly, also launch an evaluation job by running:
CUDA_VISIBLE_DEVICES=4 python eval.py \
--base_folder $EXPERIMENT_PATH \
--gin_bindings="data_dir='$DATASET_PATH'" \
----gin_configs configs/ourdata_lego2.txt
You could see our video_show.pptx
In this file,there are some code for data processing. the conversion format between colmap and Nerf
colmap2nerf.py
the conversion format between Nerf and Nerfies and Hypernerf
createMeta.py
Data is read and generated in nerf format
data_preprocess.py
jpg picture format to png picture format
jpgtopng.py
The conversion between photo and video
photo_to_video.py
video)to_pic.py