COCO to 3D Synthetic Pipeline

Welcome to the documentation for the COCO to 3D Synthetic Pipeline project!

Overview

This project implements a comprehensive three-stage pipeline for transforming 2D COCO-style instance annotations into 3D models and generating synthetic training images.

Pipeline Stages

graph LR
    A[COCO Dataset] --> B[COCO Processing]
    B --> C[3D Generation]
    C --> D[Synthetic Rendering]
    D --> E[Synthetic Images]

1. COCO Processing: Load and process COCO-style instance annotation datasets
2. 3D Model Generation: Convert 2D annotations to synthetic 3D models
3. Synthetic Image Rendering: Generate new synthetic images from the 3D models

Key Features

• COCO Format Support: Full compatibility with COCO-style instance segmentation datasets
• Automated 3D Generation: Transform 2D masks into 3D mesh representations
• Flexible Rendering: Generate synthetic images from multiple viewpoints and lighting conditions
• Extensible Architecture: Modular design for easy customization and extension

Use Cases

• Data Augmentation: Generate additional training data for object detection and segmentation models
• Domain Adaptation: Create synthetic datasets for specific domains or scenarios
• Research: Experiment with different 3D reconstruction and rendering techniques

Quick Links

• Installation Guide
• Quick Start Tutorial
• Pipeline Overview
• API Reference
• Research References - SOTA methods for NeRF and Gaussian Splatting

Project Structure

coco-to-3d-synthetic/
├── data/                  # Data directories
├── src/                   # Source code
│   ├── coco_processing/   # COCO processing module
│   ├── 3d_generation/     # 3D generation module
│   └── synthetic_rendering/ # Rendering module
├── notebooks/             # Jupyter notebooks
└── configs/              # Configuration files

Getting Started

To get started with the COCO to 3D Synthetic Pipeline, head over to the Installation Guide to set up your environment.