Summary of Contextgs: Compact 3d Gaussian Splatting with Anchor Level Context Model, by Yufei Wang et al.
ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model
by Yufei Wang, Zhihao Li, Lanqing Guo, Wenhan Yang, Alex C. Kot, Bihan Wen
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 autoregressive model for 3D Gaussian Splatting (3DGS) compression leverages context modeling at the anchor level to efficiently encode interactions and spatial dependencies between Gaussians. Building upon existing methods that compress neural Gaussians independently, this work introduces a novel approach that divides anchors into levels, allowing already encoded anchors to inform predictions of uncoded ones. Additionally, the authors propose using low-dimensional quantized features as hyperpriors for each anchor, enabling effective entropy coding and further reducing the size of the compressed representation. Compared to vanilla 3DGS and state-of-the-art Scaffold-GS, this model achieves impressive size reductions while maintaining comparable or higher rendering quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to compress data in a special kind of computer graphics called 3D Gaussian Splatting (3DGS). Right now, people are using a method that works well but takes up too much space. The authors came up with a new idea that makes the compression more efficient by looking at how the different parts of the data relate to each other. This helps reduce the size of the compressed data without losing its quality. The new method is able to compress the data more than current methods, making it useful for people working on computer graphics. |
Keywords
» Artificial intelligence » Autoregressive
