What type of images is suitable for Geometry Rendering?
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Geometry images are particularly suitable for geometry rendering due to their ability to encode surface geometry and connectivity efficiently. Traditional geometry images face challenges in optimal reconstruction and detail preservation, prompting the development of adaptive geometry images for remeshing triangular meshes. These adaptive schemes enhance reconstruction precision by minimizing Hausdorff errors and increasing sampling points in specific regions of interest, outperforming traditional parametrization-based methods. Additionally, the adaptive geometry image (AGIM) postprocessing utility offers significant improvements in global parameterization techniques by efficiently embedding 3D surfaces onto rectangular domains, reducing reconstruction errors and enhancing rendering quality. Overall, the advancements in geometry-aware image generation methods, such as geometry to image synthesis frameworks, contribute to generating realistic images that align with desired scene structures.
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| Papers (5) | Insight |
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31 Citations | Geometry Images (GIMs) and Adaptive Geometry Images (AGIMs) are suitable for Geometry Rendering as they efficiently embed 3D surfaces onto rectangular domains, reducing reconstruction errors and improving PSNR. |
| Adaptive geometry images are suitable for geometry rendering, offering optimal reconstruction errors and preserving selected geometric details, overcoming traditional parametrization limitations in remeshing triangular meshes. | |
| Geometry images are suitable for geometry rendering due to their compact encoding of surface geometry and implicit connectivity, making them efficient for hardware rendering, level of detail, and filtering. | |
Open access•Posted Content | The Geometry to Image Synthesis (GIS) framework is suitable for generating natural images from 3D scenes by leveraging scene information like geometry and segmentation, ensuring realistic and consistent image structures. |
| The Geometry to Image Synthesis (GIS) framework is suitable for generating natural images with consistent overall structure by leveraging scene information like geometry and segmentation. |
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