Generate 3D room layouts from RGB panoramas
Create recursive 3D polygons and mathematical surfaces
Generate 3D models and videos from images
Generate interactive 3D torus knots in a virtual environment
MoGe live demo
Create a recursive 3D DNA strand visualization
Display 3D models using your webcam
TRELLIS is a large 3D asset generation model.
Scalable and Versatile 3D Generation from images
Create 3D models from images using depth estimation
Generate 3D models from images
Create a dynamic and colorful 3D scene with random objects and lights
Explore a 3D model of Minnesota
3D Room Layout Estimation LGT-Net is a deep learning model designed to generate 3D room layouts from RGB panoramas. It leverages advanced computer vision techniques to accurately reconstruct the spatial structure of indoor environments, including walls, floors, and ceilings, from a single 360-degree image. This tool is particularly useful for applications in 3D modeling, architecture, interior design, and robotics.
• 2D to 3D Conversion: Converts RGB panoramas into detailed 3D room layouts.
• Automatic Wall and Floor Detection: Accurately identifies and reconstructs room boundaries.
• High Accuracy: Delivers precise 3D reconstructions even from noisy or incomplete inputs.
• Customizable Output: Allows adjustment of layout complexity and detail level.
• Integration with 3D Tools: Compatible with popular 3D modeling software for further design and editing.
• Cross-Device Compatibility: Runs efficiently on both desktop and mobile devices.
1. What type of input does LGT-Net require?
LGT-Net requires 360-degree RGB panoramas in equirectangular format. Ensure the input image is clear and well-lit for optimal results.
2. Can I customize the output layout?
Yes, LGT-Net allows customization of the output layout, including adjusting the level of detail and simplifying complex shapes for specific applications.
3. Is LGT-Net suitable for real-time applications?
While LGT-Net is efficient, it is primarily designed for offline processing. For real-time applications, additional optimizations or hardware acceleration may be required.