Summary of Gdgs: Gradient Domain Gaussian Splatting For Sparse Representation Of Radiance Fields, by Yuanhao Gong
GDGS: Gradient Domain Gaussian Splatting for Sparse Representation of Radiance Fields
by Yuanhao Gong
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 paper proposes a novel approach to 3D Gaussian splatting methods by modeling the gradient of the original signal instead of working directly on it. This sparsity-based method reduces the number of Gaussian splats required, leading to more efficient storage and improved computational performance during both training and rendering. The gradients can be used to recover 2D images via solving a Poisson equation with linear computation complexity. Experimental results confirm the sparseness of the gradients and the computational benefits of the proposed method. Applications include human body modeling and indoor environment modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computer graphics faster and more efficient. Right now, computer graphics models are very dense and take a lot of processing power to render. The researchers came up with an innovative idea: instead of working directly with the dense model, they model the gradient of the original signal. This makes the data much sparser, which means it takes less processing power to work with. As a result, rendering becomes much faster – in fact, it’s 100 to 1,000 times faster! The method can be used for various applications like modeling human bodies or indoor environments. |
