M1 Gun (155 mm) 3D Model

High-quality 3D model of the M1 Gun 155 mm cannon. Accurate geometry and detailed components for realistic visualization in 3D scenes, presentations, and game environments. Ideal for designers, educators, and enthusiasts needing a reliable artillery model in English.

M1 Gun (155 mm) 3D Model main preview

Overview

Get a high-quality M1 Gun (155 mm) Cannon 3D model designed for realistic visualization and flexible production workflows. This detailed 3D asset represents a 155 mm M1-style artillery cannon, optimized for use in games, military visualization, training simulations, architectural/technical displays, and 3D content creation.

Usage patterns:

  • Military scene building: Use the cannon as a centerpiece for realistic artillery environments, battlefields, and defense-themed renders.
  • Game development & real-time scenes: Ideal for creating interactive weapons, props, and battle assets in Unreal Engine and other real-time pipelines.
  • Animation & cinematics: Great for weapon shots, pull-out views, loading/aiming sequences, and story-driven visual projects.
  • Visualization & training content: Perfect for educational models, technical demonstrations, and simulation-ready 3D references.
  • Product mockups & presentations: Suitable for close-up renders, rotating turntables, and design/tech reporting visuals.

File formats included: Download the model in popular formats to match your pipeline: MAX, OBJ, FBX, C4D, and BLEND.

Software support: This 3D cannon asset works across major 3D and real-time platforms, including Blender, 3ds Max, Maya, Cinema 4D, Unreal Engine, and other compatible 3D software.

Bring realism and scale to your projects with this versatile M1 155 mm cannon 3D model—ready for rendering, animation, and interactive use.

Uploaded by Dave daSilva on November 2001

Tags

License

  • Royalty-Free License.
  • Commercial and editorial use according to site license terms.
  • Redistribution of source files is not allowed.
  • Please review the full license details before using the model in published work.

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