Generate 3D room layouts from RGB panoramas
Play a 3D interactive card game with drag-and-drop functionality
Scalable and Versatile 3D Generation from images
The AniMer Demo
Display 3D food models
Generate textured 3D models from text descriptions
Sparse-view SFM-free Gaussian Splatting in Seconds
Display 3D models using your webcam
Render beautiful graphics with Moondream WebGPU
Create a 3D scene with random torus knots and lights
Create a dynamic 3D scene with random shapes and lights
Generate a 3D model and video from images
Create a 3D model from an image in 10 seconds!
3D Room Layout Estimation LGT-Net is a deep learning-based model designed to predict 3D room layouts from RGB panoramas. It leverages advanced neural networks to infer spatial structures, walls, floors, and ceilings from 2D panoramic images, providing a 3D reconstruction of indoor environments. This technology is particularly useful in fields like virtual reality, real estate, and gaming.
• Deep Learning Architecture: Utilizes neural networks to process and analyze RGB panoramas. • 3D Layout Generation: Converts 2D panoramic images into editable 3D room layouts. • Automatic Room Size Calculation: Estimates room dimensions and spatial relationships. • High Accuracy: Delivers precise wall and floor detection for accurate 3D models. • Integration Capability: Works seamlessly with 3D modeling tools for further customization.
What input format does LGT-Net accept?
LGT-Net requires RGB panoramic images as input, typically in formats like PNG or JPEG.
Do I need 3D modeling expertise to use LGT-Net?
No, the model is designed to be user-friendly. Even novice users can generate 3D layouts without advanced knowledge of 3D modeling.
What are the supported output formats for the 3D layouts?
The tool supports multiple formats, including OBJ, PLY, and FBX, making it compatible with various 3D software.