Generate 3D room layouts from RGB panoramas
Generate dynamic 3D torus knot shapes
Generate 3D models from text prompts
Generate 3D models from images
Generate 3D recursive polygons and math functions
Create a dynamic torus knot scene with random properties
Generate a 3D scene with dynamic lights and torus knots
Create a dynamic 3D scene with moving lights and shapes
text-to-3D & image-to-3D
Create and explore 3D recursive polygons and math functions
Generate 3D model from Mars surface image
Generate 3D models and videos from images
generate any 3d looking art in 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.