What version control tools are best suited for VR projects?

For VR projects, the best version control tools are those that handle large files, support collaboration, and integrate with common development workflows. Git with Git LFS (Large File Storage), Perforce Helix Core, and Unity Version Control (formerly Plastic SCM) are widely used. These tools address the challenges of managing 3D assets, binaries, and frequent updates typical in VR development. While Git is popular for code, extensions like Git LFS help manage large files, but alternatives like Perforce or Plastic SCM often provide better scalability for teams working with heavy assets.

Perforce Helix Core is a strong choice for VR projects due to its ability to handle large binary files, such as 3D models, textures, and compiled builds. It supports file locking, which prevents conflicts when multiple users edit the same asset—a common issue in VR workflows. Perforce scales well for teams of all sizes and integrates with engines like Unreal Engine, which is widely used in VR development. For example, AAA game studios often rely on Perforce for its performance with terabytes of data, making it suitable for complex VR environments. However, its setup and licensing costs can be a barrier for smaller teams or indie developers.

Unity Version Control (Plastic SCM) is another top option, especially for teams using Unity, a leading engine for VR. It combines distributed and centralized version control, offering flexibility for both code and large assets. Plastic SCM includes visual diff tools for 3D files, making it easier to track changes in scenes or models. Cloud-based hosting simplifies collaboration, and its integration with Unity allows developers to manage assets directly within the editor. For non-technical team members, such as artists, Plastic SCM’s graphical interface reduces the learning curve. While cloud storage solutions like Google Drive are sometimes used for backups, they lack proper version control features like branching or conflict resolution, making them unsuitable as primary tools. Choosing the right tool depends on team size, project complexity, and existing workflows.