Geometric Erwin Model

A research project focused on developing a novel geometric deep learning model for 3D point cloud processing.

This project, undertaken as part of a group project, focused on the extension of Erwin, a novel deep learning model for processing 3D point clouds. The primary goal was to create a model that effectively captures both local and global geometric features from unstructured 3D data, while being invariant to certain symmetries, which is a significant challenge in the field of geometric deep learning.

We successfully designed, implemented, and benchmarked a new architecture for 3D point cloud analysis, contributing to the ongoing research in geometric deep learning.

The Solution

Our group developed GeoErwin, a model that leverages Erwin and GATr. This approach allows the model to learn features at different scales, from fine-grained local details to broader, long-range dependencies, while retaining the geometric structure of the data. The key components of the project included:

  • Geometric Components: Integrating geometric algebra transformer components into the pre-existing Erwin architecture, while retaining its core functionality, to enhance the model’s ability to model geometric relationships.
  • Ablation Studies: Conducting ablation studies to understand the impact of different components and configurations on model performance.

My Role and Technologies Used

As a theory and code-focused member of the team, I was actively involved in the design and implementation of the Erwin model. Our collaborative effort was central to the project’s success, from initial concept discussions to final evaluations.

Core Technologies

The project was built entirely in Python, relying on the rich ecosystem of libraries for scientific computing and deep learning:

  • Deep Learning: PyTorch was the framework of choice for building and training our custom neural network architectures.
  • Scientific Computing: Libraries like NumPy were used extensively for numerical operations and data manipulation.
  • Experiment Management: We developed a structured experimental setup to ensure our results were reproducible and comparable to existing literature.

What I Learned

This project was an immersive experience in deep learning research and collaborative development. The main takeaways for me were:

  • Geometric Deep Learning: Gaining a deeper, practical understanding of the challenges and techniques involved in applying deep learning to 3D geometric data.
  • Team-Based Research: Experiencing the power of collaboration in a research setting, where sharing ideas and peer-reviewing work leads to a stronger outcome.
  • From Idea to Implementation: Translating a novel research concept from a high-level idea into a fully functional deep learning model.
  • Systematic Experimentation: Learning how to design and execute experiments rigorously to validate a new model and compare it against established benchmarks.

How My Skills Can Help Your Business

The experience from this project has equipped me with skills that are valuable for any organization looking to innovate with data:

  • Specialized AI Solutions: Ability to design and build custom AI models tailored to specific, non-standard data types, such as 3D, graph, or other structured data.
  • Collaborative Development: A strong track record of working effectively in a team to tackle complex technical challenges.
  • Specialized Model Development: Expertise in the post-conception lifecycle of a model, from refactoring and optimization to deployment.

If your business needs to extract insights from complex datasets or requires custom AI solutions, I have the research-driven and collaborative mindset to help you succeed. Please feel free to contact me to discuss your project.