Summary of Intrinsic Papr For Point-level 3d Scene Albedo and Shading Editing, by Alireza Moazeni et al.
Intrinsic PAPR for Point-level 3D Scene Albedo and Shading Editing
by Alireza Moazeni, Shichong Peng, Ke Li
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 A recent advancement in neural rendering has successfully synthesized novel views from multi-view RGB images. However, these approaches often lack the capability to edit scene shading or color at a detailed point level while maintaining consistency across different viewpoints. This paper addresses the challenge of point-level 3D scene albedo and shading editing from multi-view RGB images by focusing on detailed editing at the point level rather than part or global levels. The approach builds upon recent advancements in point-based neural rendering, specifically Proximity Attention Point Rendering (PAPR). In contrast to other point-based methods that rely on complicated shading models or simplistic priors, this method directly models scene decomposition into albedo and shading components for better estimation accuracy. Comparative evaluations demonstrate that the proposed Intrinsic PAPR achieves higher-quality novel view rendering and superior point-level albedo and shading editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create more realistic images from different angles by changing how objects look in a 3D scene. Currently, we can only make simple changes to an image’s color or brightness. The goal is to edit the color of specific parts of the scene while keeping everything else consistent. Some methods try to break down the scene into its basic components like lighting and object color. But this approach doesn’t work well because it relies on assumptions that might not be true for all scenes. Instead, this paper develops a new method called Intrinsic PAPR that directly breaks down the scene into these components for more accurate results. |
Keywords
* Artificial intelligence * Attention
