[Research/Diploma Project] Seeking advice for fine-tuning TripoSR for educational use case

Hello TripoSR team and community!

I'm a ITMO University student working on my diploma project focused on adapting 3D reconstruction models for educational purposes. I've chosen TripoSR as the foundation for my project and would appreciate your advice on several aspects.

Project Context

• Goal: Create an educational application for graphic design students
• Users: University students with varying hardware capabilities (including integrated graphics)
• Use case: Fast 3D model generation from reference images for design projects

Specific Questions 1. Fine-tuning Strategies What would be the recommended approach for fine-tuning TripoSR on specific object categories (e.g., furniture, architectural elements)? Are there any best practices or examples for:

• Data preparation pipeline
• Optimal training parameters
• Handling domain-specific objects

2. Hardware Constraints Many students have limited hardware (integrated GPUs, 8-16GB RAM). What are your recommendations for:

• Optimizing inference speed on CPU/limited GPU
• Reducing memory footprint without significant quality loss
• Batch processing strategies

I would appreciate suggestions from the community who might have experience with Educational deployments of AI models and similar projects or alternative approaches.

Thank you for creating this amazing open-source project! Any guidance would be immensely helpful for making 3D AI accessible to students.

Diploma candidate, Daniel Babenko