Summary of From An Image to a Scene: Learning to Imagine the World From a Million 360 Videos, by Matthew Wallingford et al.
From an Image to a Scene: Learning to Imagine the World from a Million 360 Videos
by Matthew Wallingford, Anand Bhattad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Sham Kakade, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, Ali Farhadi
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| 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 proposed research tackles the challenge of developing a computer vision system that can comprehend objects and scenes in three dimensions. The current state-of-the-art methods rely heavily on synthetic data and object-centric approaches, which are insufficient for real-world scenarios. To address this limitation, the authors suggest leveraging 360-degree videos as a source of large-scale, diverse, and multi-view data. They introduce a new dataset called 360-1M, comprising scalable corresponding frames from various viewpoints. This dataset is then used to train a diffusion-based model called Odin, which can generate novel views of real-world scenes, moving the camera through the environment to infer scene geometry and layout. The authors demonstrate improved performance on standard benchmarks for novel view synthesis and 3D reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research aims to improve computer vision by allowing machines to understand objects and scenes in three dimensions. Currently, methods rely on fake data and focus on specific objects, which isn’t enough for real-life situations. To fix this, scientists propose using 360-degree videos as a way to get lots of diverse, multi-view data. They created a new dataset called 360-1M that has many frames from different angles. This dataset helps train a special model called Odin, which can create new views of the world and even move the camera to understand how things are laid out. The results show Odin does better than other methods at making new views and reconstructing 3D scenes. |
Keywords
» Artificial intelligence » Diffusion » Synthetic data
