Adaptively sampled distance fields: a general representation of shape for computer graphics

By Sarah F. Frisken, R. N. Perry, A. Rockwood, Thouis Raymond Jones. International Conference on Computer Graphics and Interactive Techniques, 2000

705 citations recorded by the source corpus.

Adaptively Sampled Distance Fields (ADFs) are a unifying representation of shape that integrate numerous concepts in computer graphics including the representation of geometry and volume data and a broad range of processing operations such as rendering, sculpting, level-of-detail management, surface offsetting, collision detection, and color gamut correction. Its structure is uncomplicated and direct, but is especially effective for quality reconstruction of complex shapes, e.g., artistic and organic forms, precision parts, volumes, high order functions, and fractals. We characterize one implementation of ADFs, illustrating its utility on two diverse applications: 1) artistic carving of fine detail, and 2) representing and rendering volume data and volumetric effects. Other applications are briefly presented.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/33fa64f2e97b906f2826775a41f92449a416dbab

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Citations in the corpus

Simulating water and smoke with an octree data structure (2004, background): “Moreover, [Frisken et al. 2000; Perry and Frisken 2001] popularized the use of signed distance functions on octree grids.”
Multi-level partition of unity implicits (2003, methodology): “One could view our MPU representations as being similar to adaptively sampled distance fields [Frisken et al. 2000], with the difference that the MPU approach uses continuous rather than sampled functions.”
Robust repair of polygonal models (2004, background): “[Frisken et al. 2000] generate a signed distance at each grid point by computing the minimum signed distance from the grid point to each input polygon.”
Adaptively sampled particle fluids (2007, background): “In this way, our particle volume can be seen as a Lagrangian analog to adaptively sampled distance ﬁelds (ADFs) [Frisken et al. 2000].”
Automatic rigging and animation of 3D characters (2007, methodology): “To approximate the medial surface and to facilitate other computations, Pinocchio computes a trilinearly interpolated adaptively sampled signed distance ﬁeld on an octree [Frisken et al. 2000].”
Feature sensitive surface extraction from volume data (2001, methodology): “In [13, 37] the complexity of the adaptively refined octree is further reduced by subdividing cells only in curved regions of the surface.”
The status, challenges, and future of additive manufacturing in engineering (2015, neutral): “adaptive sampling strategies [197] are required to overcome the”
Dual contouring of hermite data (2002, methodology): “In particular, our octree is inspired by those used in Adaptive Distance Fields [Frisken et al. 2000; Perry and Frisken 2001] in which signs are maintained at corners of cubes in the octree.”
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes (2021, methodology): “We take inspiration from classic surface extraction mechanisms [26, 12] which use quadrature and spatial data structures storing distance values to finely discretize the Euclidean space such that simple, linear basis functions can reconstruct the geometry.”
3D distance fields: a survey of techniques and applications (2006, background): “[45] is a better choice for its representation, at the cost of more complex storage and retrieval.”
Compressed Voxel-Based Mapping Using Unsupervised Learning (2016, background): “Mitigating strategies such as cyclic buffers ([8], [9]), octrees ([13], [14]), and key-block swapping [15], have been proposed to limit the memory cost of using volumetric distance-ﬁelds in very different ways.”
A survey of the marching cubes algorithm (2006, methodology): “…and dynamics (e.g., [11]), natural phenomena rendering (e.g., [12]), visualization algorithm analysis (e.g., [13]), etc. Processing involving depth maps (e.g., [14]) has also been influenced by MC isosurface extraction, especially in the development of methods based on distance fields (e.g., [15]).”