Summary of Nerf-mae: Masked Autoencoders For Self-supervised 3d Representation Learning For Neural Radiance Fields, by Muhammad Zubair Irshad et al.
NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields
by Muhammad Zubair Irshad, Sergey Zakharov, Vitor Guizilini, Adrien Gaidon, Zsolt Kira, Rares Ambrus
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of neural fields to computer vision and robotics, leveraging their ability to understand three-dimensional visual scenes. Specifically, it investigates whether masked autoencoders can be used for self-supervised pretraining of neural field models to generate effective three-dimensional representations from posed RGB images. The authors employ standard 3D Vision Transformers, utilizing the volumetric grid of Neural Radiance Fields (NeRFs) as input. They propose a novel approach that canonicalizes scenes across domains by extracting an explicit representation and employing camera trajectories for sampling. The paper presents NeRF-MAE, a self-supervised pretraining method that outperforms existing scene understanding baselines on the Front3D and ScanNet datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses neural fields to understand 3D visual scenes in computer vision and robotics. It asks if masked autoencoders can be used to make neural field models better at generating 3D representations from posed RGB images. The authors try a new way of using transformers for this task, feeding them information about the 3D scene instead of just its parts. They also find a way to take scenes from different angles and still learn something useful from them. This lets their method do well on tasks like recognizing objects in 3D environments. |
Keywords
» Artificial intelligence » Mae » Pretraining » Scene understanding » Self supervised
