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70 lines
3.5 KiB
Markdown
70 lines
3.5 KiB
Markdown
# NeRF++
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Codebase for arXiv preprint ["NeRF++: Analyzing and Improving Neural Radiance Fields"](http://arxiv.org/abs/2010.07492)
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* Work with 360 capture of large-scale unbounded scenes.
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* Support multi-gpu training and inference with PyTorch DistributedDataParallel (DDP).
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* Optimize per-image autoexposure (**experimental feature**).
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## Demo
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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).
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* 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.
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* Intrinsics and poses are stored as flattened 4x4 matrices (row-major).
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* Pixel coordinate of an image's upper-left corner is (column, row)=(0, 0), lower-right corner is (width-1, height-1).
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* Poses are camera-to-world, not world-to-camera transformations.
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* Opencv camera coordinate system is adopted, i.e., x--->right, y--->down, z--->scene. Similarly, intrinsic matrix also follows Opencv convention.
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* To convert camera poses between Opencv and Opengl conventions, the following code snippet can be used for both Opengl2Opencv and Opencv2Opengl.
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```python
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import numpy as np
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def convert_pose(C2W):
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flip_yz = np.eye(4)
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flip_yz[1, 1] = -1
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flip_yz[2, 2] = -1
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C2W = np.matmul(C2W, flip_yz)
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return C2W
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```
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* Scene normalization: move the average camera center to origin, and put all the camera centers inside the unit sphere.
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## Create environment
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```bash
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conda env create --file environment.yml
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conda activate nerfplusplus
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```
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## Training (Use all available GPUs by default)
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```python
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python ddp_train_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt
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```
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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 \
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--render_splits test,camera_path
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```
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**Note**: due to restriction imposed by torch.distributed.gather function, please make sure the number of pixels in each image is divisible by the number of GPUs if you render images parallelly.
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## Citation
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Plese cite our work if you use the code.
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```python
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@article{kaizhang2020,
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author = {Kai Zhang and Gernot Riegler and Noah Snavely and Vladlen Koltun},
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title = {NeRF++: Analyzing and Improving Neural Radiance Fields},
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journal = {arXiv:2010.07492},
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year = {2020},
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}
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```
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## Generate camera parameters (intrinsics and poses) with [COLMAP SfM](https://colmap.github.io/)
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You can use the scripts inside *'colmap_runner/'* to generate camera parameters from images with COLMAP SfM.
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* Specify *'img_dir'* and *'out_dir'* in *'colmap_runner/run_colmap.py'*.
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* Inside *'colmap_runner/'*, execute command *'python run_colmap.py'*.
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* After program finishes, you would see the posed images in the folder *'out_dir/posed_images'*.
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* Distortion-free images are inside *'out_dir/posed_images/images'*.
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* Raw COLMAP intrinsics and poses are stored as a json file *'out_dir/posed_images/kai_cameras.json'*.
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* Normalized cameras are stored in *'out_dir/posed_images/kai_cameras_normalized.json'*. See the *'Scene normalization method'* in the *'Data'* section.
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* Split distortion-free images and their correspoinding normalized cameras according to your need.
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