Summary of Gs-id: Illumination Decomposition on Gaussian Splatting Via Diffusion Prior and Parametric Light Source Optimization, by Kang Du et al.
GS-ID: Illumination Decomposition on Gaussian Splatting via Diffusion Prior and Parametric Light Source Optimization
by Kang Du, Zhihao Liang, Zeyu Wang
First submitted to arxiv on: 16 Aug 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 We introduce GS-ID, a novel framework for illumination decomposition on Gaussian Splatting, enabling photorealistic novel view synthesis and intuitive light editing. The challenge lies in estimating attributes for physically based rendering without priors for geometry and material. Our approach first employs intrinsic diffusion priors to estimate these attributes, then divides the illumination into environmental and direct components for joint optimization. We use deferred rendering to reduce computational load and learnable environment maps with Spherical Gaussians (SGs) to represent light sources parametrically. This allows controllable and photorealistic relighting on Gaussian Splatting. Our framework achieves state-of-the-art illumination decomposition results while improving geometry reconstruction and rendering performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a new way to make images look more realistic when changing the lighting. This is hard because we need to figure out what’s happening with light and shadows without knowing too much about the scene. Our approach first helps us understand how things are made up, then breaks down the light into different parts for easier processing. We use special maps and shapes to represent light in a way that makes it easy to control and make look good. This works really well and creates images that are more realistic than before. |
Keywords
» Artificial intelligence » Diffusion » Optimization
