Convert 2D image to 3D model
Generate 3D models from images
Demo for Amodal3R reconstruction
Generate and view 3D models with HuggingFab
3D Generation from text prompts
Generate a 3D model from an image
Convert 2D images to 3D models
Generate 3D models from text or images
Convert image to 3D model or video
Turn 2D images into 3D models
Convert images to 3D models
Image to 3D with DPT + 3D Point Cloud
PPSurf converting point clouds to meshes
3D Room Layout Estimation LGT-Net is a cutting-edge neural network designed to convert 2D sketches into 3D models. It specializes in estimating the layout of indoor spaces by analyzing 2D images and generating accurate 3D representations. This tool is particularly useful for applications in architecture, interior design, and robotics, where understanding spatial relationships is crucial.
• Advanced 2D-to-3D conversion: Converts 2D floor plans or sketches into detailed 3D room layouts.
• End-to-end deep learning: Utilizes neural networks for precise and efficient layout estimation.
• Automatic layout estimation: Requires minimal user input while delivering accurate results.
• Seamless integration: Works with various design and modeling software to enhance workflows.
What formats does LGT-Net support?
LGT-Net supports common image formats like PNG, JPG, and BMP for 2D inputs, and OBJ or STL for 3D outputs.
How accurate is the 3D layout estimation?
The accuracy depends on the quality of the input image. High-resolution images with clear features yield better results.
Can I use LGT-Net for outdoor spaces?
LGT-Net is optimized for indoor spaces. For outdoor environments, consider specialized tools designed for larger-scale 3D reconstruction.