3759

GPU-accelerated Adaptively Sampled Distance Fields

Thiago Bastos, Waldemar Celes
TecGraf – Computer Graphics Technology Group, Department of Computer Science, PUC-Rio, Brazil
IEEE International Conference on Shape Modeling and Applications, 2008. SMI 2008

@article{bastos2008gpu,

   title={GPU-accelerated adaptively sampled distance fields},

   author={Bastos, T. and Celes, W.},

   year={2008},

   publisher={IEEE}

}

Download Download (PDF)   View View   Source Source   

814

views

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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: