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Conference Paper
RGB-D Surface Reconstruction using 3D Gaussian Splatting
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- Authors
- Hye Sun Kim, Sung Jin Hong, Cho Rong Yu, Youn Hee Gil
- Issue Date
- 2024-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.2222-2223
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Recent advancements in RGB-D sensors and deep learning technologies have significantly improved 3D surface reconstruction, which is critical for VR/AR applications. This paper introduces an efficient RGB-D 3D surface reconstruction technique tailored for these applications. The method is based on the assumption that there is a brief period available before content delivery for capturing and reconstructing the environment. It involves a two-step approach: first, using RGBD SLAM for real-time camera pose estimation, followed by refinement of the reconstruction using 3D Gaussian Splatting. This approach enhances the initial 3D point cloud data and camera pose information to achieve high-quality results. Experimental results demonstrate that this technique substantially improves surface reconstruction quality while enabling rapid processing, allowing users to access detailed and realistic VR/AR content with minimal delay.
- KSP Keywords
- 3D point cloud data, 3D surface reconstruction, AR applications, Camera pose estimation, High-quality, Learning Technology, RGB-D sensor, Real-time, Reconstruction quality, Reconstruction technique, content delivery