Summary of An Optimization Framework to Enforce Multi-view Consistency For Texturing 3d Meshes, by Zhengyi Zhao and Chen Song and Xiaodong Gu and Yuan Dong and Qi Zuo and Weihao Yuan and Liefeng Bo and Zilong Dong and Qixing Huang
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes
by Zhengyi Zhao, Chen Song, Xiaodong Gu, Yuan Dong, Qi Zuo, Weihao Yuan, Liefeng Bo, Zilong Dong, Qixing Huang
First submitted to arxiv on: 22 Mar 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 paper introduces an optimization framework to achieve multi-view consistency in texturing 3D meshes using pre-trained text-to-image models. The framework consists of four stages: generating over-complete sets of 2D textures, selecting mutually consistent views, performing non-rigid alignment, and associating mesh faces with selected views. This approach is shown to significantly outperform baseline methods both qualitatively and quantitatively. The paper uses semi-definite programs and MRF problems to solve the optimization challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in 3D modeling called texture mapping. It helps make sure that when you see an object from different angles, it looks consistent and realistic. The method works by taking many pictures of the object from different views, then aligning them together like a puzzle. This makes the final result look much better than before. The paper shows that this approach works really well and is better than other methods tried so far. |
Keywords
» Artificial intelligence » Alignment » Optimization
