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update-hypernerf-guidance

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guanjunwu 2024-03-25 01:30:46 +08:00
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README.md
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@ -25,6 +25,7 @@ Light Gaussian implementation: [This link](https://github.com/pablodawson/4DGaus
## News ## News
2024.3.25: Update guidance for hypernerf and dynerf dataset.
2024.03.04: We change the hyperparameters of the Neu3D dataset, corresponding to our paper 2024.03.04: We change the hyperparameters of the Neu3D dataset, corresponding to our paper
@ -104,10 +105,28 @@ python scripts/downsample_point.py data/dynerf/cut_roasted_beef/colmap/dense/wor
# Finally, train. # Finally, train.
python train.py -s data/dynerf/cut_roasted_beef --port 6017 --expname "dynerf/cut_roasted_beef" --configs arguments/dynerf/cut_roasted_beef.py python train.py -s data/dynerf/cut_roasted_beef --port 6017 --expname "dynerf/cut_roasted_beef" --configs arguments/dynerf/cut_roasted_beef.py
``` ```
For training hypernerf scenes such as `virg/broom`, run
```python
# First, computing dense point clouds by COLMAP
bash colmap.sh data/hypernerf/virg/broom2 hypernerf
# Second, downsample the point clouds generated in the first step.
python scripts/downsample_point.py data/hypernerf/virg/broom2/colmap/dense/workspace/fused.ply data/hypernerf/virg/broom2/points3D_downsample2.ply
# Finally, train.
python train.py -s data/hypernerf/virg/broom2/ --port 6017 --expname "hypernerf/broom2" --configs arguments/hypernerf/broom2.py
```
For your custom datasets, install nerfstudio and follow their colmap pipeline.
```python
pip install nerfstudio
# computing camera poses by colmap pipeline
ns-process-data images --data data/your-data --output-dir data/your-ns-data
cp -r data/your-ns-data/images data/your-ns-data/colmap/images
python train.py -s data/your-ns-data/colmap --port 6017 --expname "custom" --configs arguments/hypernerf/default.py
```
You can customize your training config through the config files. You can customize your training config through the config files.
Checkpoint ## Checkpoint
Also, you can training your model with checkpoint. Also, you can training your model with checkpoint.
@ -138,17 +157,7 @@ You can just run the following script to evaluate the model.
python metrics.py --model_path "output/dnerf/bouncingballs/" python metrics.py --model_path "output/dnerf/bouncingballs/"
``` ```
## Custom Datasets
Install nerfstudio and follow their colmap pipeline.
```
pip install nerfstudio
ns-process-data images --data data/your-data --output-dir data/your-ns-data
cp -r data/your-ns-data/images data/your-ns-data/colmap/images
python train.py -s data/your-ns-data/colmap --port 6017 --expname "custom" --configs arguments/hypernerf/default.py
```
## Viewer ## Viewer
[Watch me](./docs/viewer_usage.md) [Watch me](./docs/viewer_usage.md)
## Scripts ## Scripts