threestudio-project/threestudio

Features

• 3D Asset Generators - Provides a comprehensive framework for synthesizing 3D meshes and textures from 2D image inputs and text prompts.
• Generative 3D Modeling - Provides a comprehensive framework for synthesizing 3D shapes and textures from text or images.
• Coordinate-Based Neural Representations - Defines 3D geometry and appearance using coordinate-based neural networks for density and color fields.
• Distributed GPU Training - Scales 3D model optimization across multiple graphics cards to accelerate computation and handle large datasets.
• Image-Conditioned 3D Generation - Creates 3D assets from a single input image using multi-view diffusion priors.
• Radiance Field Training Pipelines - Implements a framework for training implicit volume representations and radiance fields with multi-GPU support.
• Score Distillation Sampling - Uses 2D diffusion model gradients to guide the optimization of 3D representations toward target text prompts.
• Text-to-3D Generators - Synthesizes three-dimensional geometry and textures from natural language descriptions using diffusion models.
• Geometry Backends - Supports hybrid geometry backends including signed distance functions, tetrahedra grids, and volume grids.
• Generative Text-to-3D Pipelines - Implements end-to-end workflows for iteratively optimizing 3D geometry and textures from text prompts.
• Differentiable Geometry Renderers - Implements a differentiable rendering pipeline that allows gradients to flow from 2D images back to 3D geometry.
• Differentiable Volume Rendering - Produces 2D images from 3D volumes using configurable sample point estimators for optimization.
• Implicit Volume Representations - Defines 3D shapes using neural density fields and feature dimensions for implicit volume representation.
• Signed Distance Field Representations - Implements 3D surface modeling using Signed Distance Field representations.
• Single-Image 3D Reconstructions - Generates complete 3D models and meshes from a single input image using multi-view diffusion priors.
• Tetrahedra Grid Representations - Models geometry using a tetrahedra SDF grid with surface extraction and vertex optimization.
• Volume Grid Representations - Creates explicit geometry using a feature volume grid for density and material properties.
• Experiment Management Interfaces - Ships an interactive interface for managing generative experiments, including model selection and guidance scale configuration.
• Patch-Based Training Strategies - Reduces memory usage during high-resolution training by combining global images with high-resolution local patches.
• Spatial Control Interfaces - Provides a graphical interface to control configurations and monitor the progress of 3D generation training.
• Model Training Management Interfaces - Provides a web-based interface to configure model settings, trigger training, and save checkpoints.
• Patch Rendering - Implements memory-efficient local patch rendering to enable high-resolution 3D asset training.
• Materials & Shading - Implements realistic lighting and surface properties using physical, shaded, or neural network-based material systems.
• Physically Based Rendering Materials - Implements realistic lighting by learning albedo, metallic, and roughness properties.
• Neural Radiance Materials - Simulates view-dependent lighting effects using a network-based material system.
• Mesh Exporters - Enables exporting generated geometry to standard files including UV maps, vertex normals, and texture data.
• Modular Plugin Systems - Features a modular plugin system that loads external scripts to extend the framework with custom nodes.
• 3D Generative Experiment Interfaces - Provides a web-based interface for managing experiments, configuring model settings, and monitoring training progress.
• ML Experiment Management Interfaces - Provides a web-based interface for configuring hyperparameters and monitoring training progress with real-time previews.

Star-Verlauf