Generate 3D room layouts from RGB panoramas
Generate 3D models from text prompts
create 3d-gltf face-mesh from image with mediapipe
Generate 3D models from single images
text-to-3D & image-to-3D
Display fractal patterns using L-systems
Create 3D models and videos from images
Play interactive 3D Pyramids game
Create 3D reconstructions from images using MASt3R and 3DGS
Create 3D mesh by chatting.
Generate 3D models from images
Create interactive 3D scenes with torus knots
Text-to-3D and Image-to-3D Generation
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.