3D高斯溅射环境配置

### 3D Gaussian Splatter Environment Setup Configuration

For configuring the environment to work with 3D Gaussian Splatting, several dependencies and configurations must be set up correctly. The process involves installing necessary software packages as well as setting up a development environment that can compile and run C++ code along with Python scripts.

#### Prerequisites Installation
To begin, ensure that Git is installed on your system since it will be used for cloning repositories from GitHub[^2]. Additionally, install CUDA Toolkit if GPU acceleration support is required; this toolkit facilitates running computations on NVIDIA GPUs which significantly speeds up processing times during training or rendering phases of 3D Gaussians[^1].

#### Setting Up Development Tools
A compatible version of CMake should also be present because it manages building projects written in languages like C++. Furthermore, PyTorch needs installation alongside torchvision libraries due to their extensive usage within deep learning models involved in optimizing radiance fields represented by these gaussians.

pip install torch torchvision torchaudio

#### Cloning Repository & Installing Dependencies
After preparing prerequisites mentioned above, proceed by cloning the official repository using git clone command provided below:

git clone https://github.com/graphdeco-inria/gaussian-splatting.git
cd gaussian-splatting
conda env create -f environment.yml
conda activate gaussian_splatting_env

This sequence installs all Python package requirements specified inside `environment.yml` file found at root directory after cloning.

#### Building Project From Source Code
Finally, build project binaries through invoking cmake followed by make commands under appropriate subdirectories where source files reside according to instructions documented online:

mkdir build && cd build
cmake ..
make -j$(nproc)

These steps prepare an operational setup capable of executing tasks related to real-time radiance field rendering via 3D Gaussian representations described earlier.

--related questions--

1. What are some common issues encountered while compiling open-source computer vision projects?
2. How does one choose between different versions of dependent libraries when creating machine learning environments?
3. Can you explain what role each component plays in achieving efficient scene representation using sparse point clouds transformed into dense distributions modeled as collections of ellipsoids?
4. In practice, how much performance improvement could leveraging GPU hardware bring compared against CPU-only implementations concerning large-scale data processing applications such as those seen here?