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4 years ago
# NeRF++
Codebase for paper:
* Work with 360 capture of large-scale unbounded scenes.
* Support multi-gpu training and inference.
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* Optimize per-image autoexposure (ongoing work)
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## 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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conda activate nerf-ddp
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```
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## Training (Use all available GPUs by default)
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```python
python ddp_train_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt
```
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## Testing (Use all available GPUs by default)
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```python
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python ddp_test_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt \
--render_splits test,camera_path
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```