zubair-irshad/NeRF-MAE
[ECCV 2024] Pytorch code for our ECCV'24 paper NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields
This project helps researchers and developers working with 3D scene reconstruction and understanding. It takes Neural Radiance Fields (NeRFs) as input and processes them to extract valuable 3D features. The output is a set of multi-scale feature embeddings that can be used for various downstream tasks like 3D object detection. It is ideal for those developing advanced 3D computer vision applications.
104 stars. No commits in the last 6 months.
Use this if you need to extract robust, high-level 3D features from NeRF scenes without extensive labeled data, especially for tasks like 3D object detection or scene classification.
Not ideal if your primary need is general image processing or if you are not working with Neural Radiance Fields (NeRFs) as your core 3D scene representation.
Stars
104
Forks
4
Language
Python
License
MIT
Category
Last pushed
Mar 20, 2025
Commits (30d)
0
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