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Sushmey/
shapenet-model

Name: shapenet-model
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2D to 3D Reconstruction System

A comprehensive system for reconstructing 3D objects from 2D images using deep learning. This system is designed to work with ShapeNet datasets and provides both single-view and multi-view reconstruction capabilities.

Features

Multi-format 3D Output: Generates voxel grids, point clouds, and meshes
Flexible Architecture: Supports both single-view and multi-view reconstruction
Interactive Visualization: Web-based 3D viewers and comparison tools
Batch Processing: Process multiple images or entire directories
Comprehensive Training: Full training pipeline with validation and metrics
Easy-to-use Interface: Command-line tools and Python API

System Architecture

The system consists of several key components:

Data Preprocessing (data_preprocessing.py): Converts 3D models to 2D renderings
Model Architecture (model_architecture.py): Neural network implementations
Training Pipeline (training_pipeline.py): Training and validation loops
Inference System (inference_system.py): Model inference and 3D generation
Visualization Tools (visualization_tools.py): Interactive 3D viewers and comparisons
Main Interface (main.py): Unified command-line interface

Installation

Clone the repository:

bash
1git clone <repository-url> 2cd shapenet
Create and activate virtual environment:

bash
1python -m venv shapenet_venv 2source shapenet_venv/bin/activate # On Windows: shapenet_venv\Scripts\activate
Install dependencies:

pip install -r requirements.txt

Quick Start

1. Preprocess Your Dataset

First, preprocess your ShapeNet dataset to generate 2D renderings:

python main.py preprocess

This will:

Load 3D models from your ShapeNet dataset
Generate multiple 2D views of each model
Create training/validation/test splits
Save processed data for training

2. Train the Model

Train the reconstruction model:

python main.py train

This will:

Load the preprocessed data
Train the neural network
Save model checkpoints
Generate training curves

3. Run Inference

Generate 3D models from 2D images:

bash

# Single image
python main.py inference --input path/to/image.jpg --output results/

# Multiple images
python main.py inference --input path/to/images/ --output results/

4. Run Full Pipeline

Execute the complete pipeline:

python main.py pipeline --input path/to/test_image.jpg

Usage Examples

Python API

python

from main import ReconstructionSystem

# Initialize system
system = ReconstructionSystem()

# Preprocess data
system.preprocess_data()

# Train model
system.train_model()

# Run inference
system.run_inference('input_image.jpg', 'output_dir/')

# Evaluate model
system.evaluate_model()

Direct Module Usage

python

from inference_system import InferenceEngine
from visualization_tools import ReconstructionVisualizer

# Load trained model
engine = InferenceEngine('checkpoints/best_model.pth')

# Generate 3D from image
results = engine.generate_3d_from_single_view('image.jpg')

# Visualize results
visualizer = ReconstructionVisualizer()
fig = visualizer.create_interactive_plotly_viz(
    voxels=results['voxels'],
    pointcloud=results['pointcloud']
)
fig.show()

Configuration

Create a config.json file to customize the system:

json

{
    "data_path": "/path/to/shapenet",
    "processed_data_path": "/path/to/processed_data",
    "checkpoint_dir": "/path/to/checkpoints",
    "output_dir": "/path/to/outputs",
    "model_type": "single_view",
    "num_views": 12,
    "image_size": 224,
    "voxel_size": 32,
    "num_points": 2048,
    "batch_size": 4,
    "learning_rate": 0.001,
    "num_epochs": 100
}

Model Types

Single-View Reconstruction

Takes one 2D image as input
Faster inference
Good for real-time applications
Use model_type: "single_view"

Multi-View Reconstruction

Takes multiple 2D images as input
Higher accuracy
Uses attention mechanism
Use model_type: "multi_view"

Output Formats

The system generates 3D reconstructions in multiple formats:

Voxel Grids: 3D binary or probability grids
Point Clouds: 3D point coordinates
Meshes: 3D triangular meshes (OBJ format)
Interactive Visualizations: Web-based 3D viewers

File Structure

shapenet/
├── data_preprocessing.py      # Data preprocessing pipeline
├── model_architecture.py      # Neural network architectures
├── training_pipeline.py       # Training and validation
├── inference_system.py        # Model inference
├── visualization_tools.py     # Visualization utilities
├── main.py                    # Main interface
├── requirements.txt           # Dependencies
├── config.json               # Configuration file
├── README.md                 # This file
├── PartSym dataset/          # Your ShapeNet dataset
├── processed_data/           # Preprocessed data
├── checkpoints/              # Model checkpoints
└── outputs/                  # Inference results

Performance Tips

GPU Usage: The system automatically uses GPU if available
Batch Size: Adjust based on your GPU memory
Image Size: Larger images provide better quality but slower processing
Voxel Resolution: Higher resolution voxels are more detailed but require more memory

Troubleshooting

Common Issues

CUDA Out of Memory:
- Reduce batch size
- Use smaller image sizes
- Use CPU instead of GPU
Missing Dependencies:
- Install all requirements: pip install -r requirements.txt
- Check Python version compatibility
Data Loading Errors:
- Verify dataset path in configuration
- Check file permissions
- Ensure OBJ files are valid

Debug Mode

Enable debug logging:

python

import logging
logging.basicConfig(level=logging.DEBUG)

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this system in your research, please cite:

bibtex

@software{shapenet_reconstruction,
  title={2D to 3D Reconstruction System},
  author={Your Name},
  year={2024},
  url={https://github.com/yourusername/shapenet}
}

Acknowledgments

ShapeNet dataset for 3D models
PyTorch for deep learning framework
Trimesh and Open3D for 3D processing
Plotly for interactive visualizations

Sushmey/shapenet-model

2D to 3D Reconstruction System

Features

System Architecture

Installation

Quick Start

1. Preprocess Your Dataset

2. Train the Model

3. Run Inference

4. Run Full Pipeline

Usage Examples

Python API

Direct Module Usage

Configuration

Model Types

Single-View Reconstruction

Multi-View Reconstruction

Output Formats

File Structure

Performance Tips

Troubleshooting

Common Issues

Debug Mode

Contributing

License

Citation

Acknowledgments

Sushmey/
shapenet-model