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@@ -60,30 +60,24 @@ The optimizer uses PyTorch and CUDA extensions in a Python environment to produc
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- CUDA-ready GPU with Compute Capability 7.0+
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- 24 GB VRAM to train the largest scenes in our test suite
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-### Setup
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-
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-Our provided install method is based on Conda package and environment management. We suggest 3 options, depending on your available disk space.
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+### Software Requirements
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+- C++ Compiler (Visual Studio 2019 for Windows)
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+- CUDA 11 Developer SDK
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+- Conda (recommended for easy setup)
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-#### Option 1 (Plenty of space on system drive)
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+### Setup
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-To produce our exact evaluation environment on a freshly set up machine should be straightforward once Conda is installed (at the expense of considerable disk space):
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+Our provided install method is based on Conda package and environment management:
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```shell
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-conda env create --file environment_full.yml # This will take some time
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+conda env create --file environment.yml # This will take less time
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conda activate gaussian_splatting
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```
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-#### Option 2 (Little space on system drive)
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-If you already have a recent C++ compiler and a version of the CUDA **development** kit 11 installed, you can opt to use the lighter-weight environment install instead.
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-```shell
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-conda env create --file environment_light.yml # This will take less time
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-conda activate gaussian_splatting
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-```
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-#### Option 3 (Even less space on system drive)
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-Note that even with the light version, downloading packages and creating a new environment with Conda can require a significant amount of disk space. By default, Conda will use the main system hard drive. You can avoid this by specifying a different package download location and an environment on a different drive:
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+Note that downloading packages and creating a new environment with Conda can require a significant amount of disk space. By default, Conda will use the main system hard drive. You can avoid this by specifying a different package download location and an environment on a different drive:
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```shell
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conda config --add pkgs_dirs <Drive>/<pkg_path>
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-conda env create --file environment_light.yml --prefix <Drive>/<env_path>/gaussian_splatting
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+conda env create --file environment.yml --prefix <Drive>/<env_path>/gaussian_splatting
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conda activate <Drive>/<env_path>/gaussian_splatting
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```
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@@ -126,9 +120,14 @@ The Network Viewer can be used to observe the training process and watch the mod
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- OpenGL 4.5-ready GPU
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- 8 GB VRAM
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+### Software Requirements
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+- C++ Compiler (Visual Studio 2019 for Windows)
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+- CUDA 11 Developer SDK
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+- CMake (recent version)
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+
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### Setup
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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. For setup, you will need the CUDA 11 **development** kit, a C++ compiler (use Visual Studio **2019** on Windows) and **CMake**, then follow the steps corresponding to your operating system.
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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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#### Windows
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On Windows, CMake should take care of your dependencies
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@@ -173,6 +172,11 @@ The Real-Time Viewer can be used to render trained models with real-time frame r
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- OpenGL 4.5-ready GPU
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- 8 GB VRAM
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+### Software Requirements
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+- C++ Compiler (Visual Studio 2019 for Windows)
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+- CUDA 11 Developer SDK
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+- CMake (recent version)
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+
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### Setup
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The setup is the same as for the remote viewer.
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bkerbl