NeRF Repo Implementation & Enhancement
Budget: ₹600 – ₹1,500 INR
I have my eye on a lightweight Neural Radiance Fields repository on GitHub and I want it running reliably on my machine first, then extended with a couple of practical, well-documented features.
The core tasks start with cloning the repo, handling any build or dependency issues (it is PyTorch-based, so expect CUDA quirks), and giving me a repeatable setup script or Dockerfile so I can spin it up on a fresh system without surprises. Once the original code is fully operational, I’d like you to design and code at least one meaningful new feature—think along the lines of a user-friendly launch interface, broader dataset compatibility, or another improvement you feel would have real value. I’m open to discussing the exact direction as long as we keep the code lightweight and true to the repo’s spirit.
When you hand off, I need:
• a clean, commented pull-request-ready codebase
• a short README detailing changes, requirements, and how to run everything
• a screen-capture or notebook that demonstrates the new feature working on a sample scene
If you are comfortable debugging research repos, speak fluent Python/PyTorch, and enjoy adding polish rather than rewriting from scratch, this should be straightforward. Let me know your thoughts on the extra feature you’d propose and the timeline you’ll need.
The core tasks start with cloning the repo, handling any build or dependency issues (it is PyTorch-based, so expect CUDA quirks), and giving me a repeatable setup script or Dockerfile so I can spin it up on a fresh system without surprises. Once the original code is fully operational, I’d like you to design and code at least one meaningful new feature—think along the lines of a user-friendly launch interface, broader dataset compatibility, or another improvement you feel would have real value. I’m open to discussing the exact direction as long as we keep the code lightweight and true to the repo’s spirit.
When you hand off, I need:
• a clean, commented pull-request-ready codebase
• a short README detailing changes, requirements, and how to run everything
• a screen-capture or notebook that demonstrates the new feature working on a sample scene
If you are comfortable debugging research repos, speak fluent Python/PyTorch, and enjoy adding polish rather than rewriting from scratch, this should be straightforward. Let me know your thoughts on the extra feature you’d propose and the timeline you’ll need.
Related categories:
Python
CUDA
Machine Learning (ML)
Git
Data Science
Neural Networks
Docker
Deep Learning