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# NeRF++
Codebase for paper:
* Work with 360 capture of large-scale unbounded scenes.
* Support multi-gpu training and inference.
## Data
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* Download our preprocessed data from [tanks_and_temples ](https://drive.google.com/file/d/11KRfN91W1AxAW6lOFs4EeYDbeoQZCi87/view?usp=sharing ), [lf_data ](https://drive.google.com/file/d/1gsjDjkbTh4GAR9fFqlIDZ__qR9NYTURQ/view?usp=sharing ).
* Put the data in the sub-folder data/ of this code directory.
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* Data format.
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* Each scene consists of 3 splits: train/test/validation.
* Intrinsics and poses are stored as flattened 4x4 matrices.
* Opencv camera coordinate system is adopted, i.e., x--->right, y--->down, z--->scene.
* Scene normalization: move the average camera center to origin, and put all the camera centers inside the unit sphere.
## Create environment
```bash
conda env create --file environment.yml
```
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## Training
```python
python ddp_train_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt
```
## Testing
```python
python ddp_test_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt --render_splits test,camera_path
```