Summary of Coral Model Generation From Single Images For Virtual Reality Applications, by Jie Fu (university Of the Arts London et al.
Coral Model Generation from Single Images for Virtual Reality Applications
by Jie Fu, Shun Fu, Mick Grierson
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Human-Computer Interaction (cs.HC); Multimedia (cs.MM)
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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 deep-learning framework generates high-precision 3D coral models from a single image using the Coral dataset. The framework extracts geometric and texture features, performs 3D reconstruction, and optimizes design and material blending. Advanced optimization and polygon count control ensure shape accuracy, detail retention, and flexible output for various complexities. The framework is designed to cater to high-quality rendering and real-time interaction in VR applications. The generated models surpass traditional methods in detail, visual quality, and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a deep-learning framework that generates high-precision 3D coral models from a single image. This framework uses the Coral dataset to create detailed and realistic 3D models. The paper also incorporates Explainable AI (XAI) to transform AI-generated models into interactive “artworks” that can be viewed in VR and XR. This technology enables users to interact with the generated models in real-time, displaying information like coral species and habitat. |
Keywords
» Artificial intelligence » Deep learning » Optimization » Precision
