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@@ -92,7 +92,7 @@ If you can afford the disk space, we recommend using our environment files for s
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To run the optimizer, simply use
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```shell
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-python train.py -s <path to dataset>
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+python train.py -s <path to COLMAP or NeRF Synthetic dataset>
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```
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<details>
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@@ -168,7 +168,7 @@ The MipNeRF360 scenes are hosted by the paper authors [here](https://jonbarron.i
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### Evaluation
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By default, the trained models use all available images in the dataset. To train them while withholding a test set for evaluation, use the ```--eval``` flag. This way, you can render training/test sets and produce error metrics as follows:
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```shell
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-python train.py -s <path to dataset> --eval # Train with train/test split
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+python train.py -s <path to COLMAP or NeRF Synthetic dataset> --eval # Train with train/test split
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python render.py -m <path to trained model> # Generate renderings
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python metrics.py -m <path to trained model> # Compute error metrics on renderings
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```
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@@ -262,7 +262,7 @@ We provide two interactive iewers for our method: remote and real-time. Our view
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- 7zip (only on Windows)
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### Pre-built Windows Binaries
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-We provide pre-build binaries for Windows [here](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip). We recommend using them on Windows for an efficient setup, since the building of SIBR involves several external dependencies that must be downloaded and compiled on-the-fly.
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+We provide pre-built binaries for Windows [here](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip). We recommend using them on Windows for an efficient setup, since the building of SIBR involves several external dependencies that must be downloaded and compiled on-the-fly.
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### Installation from Source
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If you cloned with submodules (e.g., using ```--recursive```), the source code for the viewers is found in ```SIBR_viewers_(windows|linux)``` (choose whichever fits your OS). The network viewer runs within the SIBR framework for Image-based Rendering applications.
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bkerbl