GPU-accelerated Adaptively Sampled Distance Fields
Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics, computer vision and physics. ADFs are especially beneficial for representing shapes with features at very diverse scales. In this paper, we pro...
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| Published in: | 2008 IEEE International Conference on Shape Modeling and Applications pp. 171 - 178 |
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| Main Authors: | , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
01.06.2008
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| Subjects: | |
| ISBN: | 9781424422609, 1424422604 |
| Online Access: | Get full text |
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| Summary: | Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics, computer vision and physics. ADFs are especially beneficial for representing shapes with features at very diverse scales. In this paper, we propose a strategy to represent and reconstruct ADFs on modern graphics hardware (GPUs). We employ a 3D hashing scheme to store the underlying data structure and try to balance the tradeoff between memory requirements and reconstruction efficiency. To render ADFs on GPU, we use a general-purpose ray-casting technique based on sphere tracing, which guarantees the reconstruction of fine details. We also present a way to overcome the Cl discontinuities inherent to ADFs and efficiently reconstruct smooth surface normals across cell boundaries. The effectiveness of our proposal is demonstrated for isosurface rendering and morphing. |
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| ISBN: | 9781424422609 1424422604 |
| DOI: | 10.1109/SMI.2008.4547967 |

