Coral Model Generation from Single Images for Virtual Reality Applications
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910589038624768 |
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| author | Fu, Jie Fu, Shun Grierson, Mick |
| author_facet | Fu, Jie Fu, Shun Grierson, Mick |
| contents | With the rapid development of VR technology, the demand for high-quality 3D models is increasing. Traditional methods struggle with efficiency and quality in large-scale customization. This paper introduces a deep-learning framework that 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, catering to high-quality rendering and real-time interaction needs.The project incorporates Explainable AI (XAI) to transform AI-generated models into interactive "artworks," best viewed in VR and XR. This enhances model interpretability and human-machine collaboration. Real-time feedback in VR interactions displays information like coral species and habitat, enriching user experience. The generated models surpass traditional methods in detail, visual quality, and efficiency. This research offers an intelligent approach to 3D content creation for VR, lowering production barriers, and promoting widespread VR applications. Additionally, integrating XAI provides new insights into AI-generated visual content and advances research in 3D vision interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coral Model Generation from Single Images for Virtual Reality Applications Fu, Jie Fu, Shun Grierson, Mick Computer Vision and Pattern Recognition Artificial Intelligence Graphics Human-Computer Interaction Multimedia With the rapid development of VR technology, the demand for high-quality 3D models is increasing. Traditional methods struggle with efficiency and quality in large-scale customization. This paper introduces a deep-learning framework that 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, catering to high-quality rendering and real-time interaction needs.The project incorporates Explainable AI (XAI) to transform AI-generated models into interactive "artworks," best viewed in VR and XR. This enhances model interpretability and human-machine collaboration. Real-time feedback in VR interactions displays information like coral species and habitat, enriching user experience. The generated models surpass traditional methods in detail, visual quality, and efficiency. This research offers an intelligent approach to 3D content creation for VR, lowering production barriers, and promoting widespread VR applications. Additionally, integrating XAI provides new insights into AI-generated visual content and advances research in 3D vision interpretability. |
| title | Coral Model Generation from Single Images for Virtual Reality Applications |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Human-Computer Interaction Multimedia |
| url | https://arxiv.org/abs/2409.02376 |