Summary of Scube: Instant Large-scale Scene Reconstruction Using Voxsplats, by Xuanchi Ren et al.
SCube: Instant Large-Scale Scene Reconstruction using VoxSplats
by Xuanchi Ren, Yifan Lu, Hanxue Liang, Zhangjie Wu, Huan Ling, Mike Chen, Sanja Fidler, Francis Williams, Jiahui Huang
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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 We introduce SCube, a novel method for reconstructing large-scale 3D scenes from sparse posed images. Our approach encodes reconstructed scenes using VoxSplat, a set of 3D Gaussians supported on high-resolution sparse-voxel scaffolds. To generate VoxSplat from images, we employ a hierarchical voxel latent diffusion model conditioned on input images, followed by a feedforward appearance prediction model. This allows us to generate millions of Gaussians with a 1024^3 voxel grid spanning hundreds of meters in just 20 seconds. Unlike prior works, SCube leverages high-resolution sparse networks and produces sharp outputs from few views, making it suitable for applications like LiDAR simulation and text-to-scene generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a few photos of a city or landscape from different angles, and then being able to recreate the entire scene in 3D. That’s what SCube, a new method, can do. It takes a set of posed images as input and generates a detailed 3D model of the scene. This is possible because SCube uses a unique combination of algorithms that work together to create a highly detailed 3D representation. The method is fast, too – it can generate millions of pixels in just 20 seconds! This technology has many potential applications, including simulating how LiDAR (a type of sensor) might see the world and generating realistic scenes from text descriptions. |
Keywords
» Artificial intelligence » Diffusion model
