How to Create Gaussian Splats with Nerfstudio

Introduction

In this tutorial, we will guide you through the process of creating Gaussian Splats using Nerf Studio. This tutorial is ideal for both beginners and those who have experience with Nerf Studio, as it covers everything from installation requirements to rendering videos and exporting files.

Chapter 1: Installation and Setup

Before you can create Gaussian Splats, ensure you have Nerf Studio installed. If you haven't installed it yet, refer to the installation video linked in the description. If you've installed it before 2024, consider reinstalling to access the latest features.

Key Steps:

• Follow the installation guide provided in the video description.
• After installation, ensure you have the necessary environment set up.

Chapter 2: Download Sample Data

To start with your Gaussian Splats, you will need sample data.

Steps:

1. Open Command Prompt (search for "CMD").
2. Activate your Nerf Studio environment:

   conda activate NerfStudio
   

3. Change directory to your Nerf Studio folder:

   cd path\to\NerfStudio
   

4. Download the sample data by running:

   NS download data
   

5. Once downloaded, check the NerfStudio/data folder for the images.

Practical Tip:

Always use provided test data for initial training to ensure everything is functioning correctly.

Chapter 3: Train the Gaussian Splats

Now that you have the data, you can start training your Gaussian Splats.

Steps:

1. In Command Prompt, enter the following command to start training:

   NS train gajin splatting --data_dir data/NerfStudio/poster
   

2. Be patient while the training loads the data. It may take several minutes depending on your system's hardware.

Common Pitfall:

If training doesn't start immediately, ensure that you are in the correct directory and that the data is properly downloaded.

Chapter 4: Launch the Beta Viewer

To visualize the training process in real-time, use the viewer.

Steps:

1. After training, launch the viewer by opening a new Command Prompt window.
2. Activate your environment and navigate to the Nerf Studio folder:

   conda activate NerfStudio
   cd path\to\NerfStudio
   

3. Launch the viewer with the appropriate configuration:

   NS viewer load-config path\to\your\config_file
   

Practical Tip:

To find your IP address, go to your network settings and look under the Wi-Fi details.

Chapter 5: Viewer Controls

Familiarize yourself with the viewer to manipulate and analyze the training data.

Key Features:

• Change resolution settings for better visualization.
• Use depth models and color changes to evaluate results.
• Crop views to focus on specific areas of interest.

Chapter 6: Render a Video

To create a video from your training session, follow these steps.

Steps:

1. Add keyframes to set the animation path.
2. Preview the render to ensure it looks as desired.
3. Generate the render command and copy it to the clipboard.
4. Open Command Prompt and paste the command to render the video.

Note:

The video will be saved in the renders folder within your Nerf Studio directory.

Chapter 7: Export Gaussian Splats

After training, you can export your Gaussian Splats for use in other applications.

Steps:

1. Run the export command:

   NS export gajin Splat --config path\to\your\config_file --output_dir path\to\output_directory
   

2. Check the designated folder for your exported files.

Chapter 8: Using Your Own Images

If you wish to use your own imagery, follow these additional steps.

Requirements:

• Install CoalMap and FFmpeg.
• Add their paths to your environment variables.

Steps:

1. Process your video or images using:

   NS process data --type video --data_path path\to\video
   

2. Train the Gaussian Splats using:

   NS train gajin splatting --data_dir path\to\processed_data
   

Conclusion

You've now learned how to create Gaussian Splats using Nerf Studio, from installation to exporting your results. As a next step, consider experimenting with your own images and further tweaking the training parameters for different results. For any questions, feel free to ask in the comments or check the GitHub repository linked in the video description. Happy splatting!