Neural Radiance Fields Project

Overview

This project explores the implementation and application of Neural Radiance Fields (NeRFs), a state-of-the-art technique in 3D scene reconstruction and rendering using deep learning. By leveraging coordinate-based neural representations, the project demonstrates the ability to synthesize novel views of complex 3D scenes from sparse 2D images.

Key Highlights

• Neural Radiance Fields Implementation:

  • Applied NeRF principles to train neural networks that represent volumetric scene functions.
  • Reconstructed 3D scenes from sparse 2D image datasets using differentiable volume rendering.

• Visualization and Analysis:

  • Rendered and visualized novel views of reconstructed scenes.
  • Included visual examples like starry_night.jpg to demonstrate the quality of results.

Skills Demonstrated

• Deep Learning for Computer Graphics:

  • Implemented and trained neural networks to model complex 3D radiance fields.
  • Utilized principles of differentiable rendering to generate high-fidelity 3D representations.

• Mathematical Modeling:

  • Worked with mathematical concepts such as volumetric rendering and coordinate-based neural representations.
  • Designed loss functions to optimize rendering quality.

• Python Programming:

  • Showcased proficiency in Python, with clean, modular code across Jupyter Notebooks and Python scripts.

• Data Handling:

  • Processed large datasets and ensured efficient data loading and manipulation for training.

Impact and Applications

• 3D Scene Reconstruction: NeRFs have transformative applications in virtual reality, gaming, and film production.
• Real-World Innovation: Demonstrates the ability to apply cutting-edge research to solve challenging 3D computer vision problems.
• Research Contributions: Provides a foundation for further exploration in neural rendering and scene representation.

Supporting Files

• Machine_Perception_Homework5.ipynb: Main notebook for implementation and experimentation.
• part1_code.py and part2_code.py: Modular Python scripts for NeRF pipeline.
• lego_data.npz: Dataset used for training the NeRF models.
• cis580_hw5_final.pdf: Documentation providing detailed explanations of the project.
• starry_night.jpg: Example visualization of the rendering output.

---

This description highlights the technical expertise and cutting-edge nature of the project, showcasing its potential impact in computer vision and graphics.