Advanced Member L'Ancien Regime Posted October 11, 2018 Advanced Member Report Share Posted October 11, 2018 https://www.madewithmischief.com/ https://community.wacom.com/inspiration/blog/2015/january/50-trillion-to-one-the-magic-behind-mischief The unique shape representation made possible by ADFs manages to be neither pixels nor vectors. It takes the best of both worlds to yield a drawing program that’s fast, high quality, and infinitely scalable with no loss of resolution. “You can take any part of this infinite canvas and expand it to any size and any resolution – you can take your doodle and expand it to the size of a billboard,” Frisken said. “There’s a 50 trillion-to-one zoom factor, which is like sitting on the moon and looking at a single flower on Earth and then drawing on a petal of that flower.” Made with Mischief is a digital pen-based drawing system designed to be used with both Apple and Windows computers that combines two of the most commonly used design and drawing processes. Users can design using colored pixels, like Photoshop, while it also has Illustrator-like tools that lets users draw what Frisken called “mathematical curves.” The outcome is the most high-tech computer drawing process I’ve seen, allowing users the fluidity of drawing by hand, and the infinite possibilities of computer design, without the limitations of either Illustrator or Photoshop. Mischief is powered by a revolutionary patented shape representation, known as Adaptively Sampled Distance Fields (ADFs), co-invented by Frisken. ADFs have several advantages for creative applications: they provide high-quality stroke rendering; they are amenable to hardware-based rendering so drawing is extremely responsive; they are very compact, resulting in small file sizes; they can be scaled without introducing pixelation artifacts; and they can accurately represent much richer and more complex shapes than traditional vector-based stroke representations. For Frisken, the acquisition of Made With Mischief by The Foundry enables her to retain her core vision of providing high-quality software tools for a wide range of artists and to preserve an accessible price point, while bringing future versions of the platform to an even broader audience. ADF https://www.fxguide.com/featured/whats-the-foundry-buying-the-tech-of-adf/ ADF actually came from research into medical imaging for applications such as anatomy education, surgical simulation and computer-assisted surgery. "1998 was the first paper that represented shape with distance," Dr Frisken explains. The team needed a way to represent three-dimensional medical examples such as the way a knee worked as part of a coordinated a multi-institutional, multi-disciplinary project to simulate arthroscopic knee surgery. Frisken served as project leader, algorithm designer, software system designer and implementor on the project. "We couldn't represent a knee as just a mathematical equation with some distance, you needed to sample the space within which that knee sat, and the distance in that space. If you just sample at a very high rate you get a huge volume of data, and that makes it slow to process and render, so we started looking at ways to reduce the number of samples you needed and we came upon using an adaptive sampling of the space." The 'adaptively' part of ADF comes directly from that initial knee problem and it has since been implemented in a host of ways from simple Oct-trees to more complex and powerful data structures. The first research was done by Frisken and Ron Perry who continues today to work on ADFs at MERL, but who also consults to the Foundry. He was key to establishing Mischief and coding the original the product. Perry is very much a key author and developer of the technology, but he also has other important research interests at MERL where is is a senior researcher. ADF vs Voxels Back to 3D and the roots of the technology. You might be wondering about the differences between traditional voxel representations and ADF distance fields in 3D? It is a complex area of 3D maths because a voxel representation can be thought of as a partitioning of space into cubes, and one can store anything in each cube (including theoretically distance). But if one accepts that most voxel implementations are not storing distance then the comparison is a little easier. A more traditional voxel representation use regularly sampled volumes and they store either binary values such as inside or outside or density values (using a density of 1 inside the shape, a density of zero outside the shape, and an average density for voxels that contain the surface). Voxels have many uses - sometimes the value is assumed to be the average of what is in the cube/box/voxel, sometimes it is assumed the value is at one corner of a 3D grid. Significantly, if one uses a discrete sample approach, then you really only get binary samples for shapes that have hard surfaces and you have to interpolate the density inside the cube when you need to locate the surface of the shape for rendering or other processing. (eg. 1 inside the shape, 0 outside). There are several problems with this approach. First, traditional voxel models were regularly sampled so you needed a lot of samples to represent shapes that have complex and compound detail, even if that detail is only on the surface or limited to a very small part of the shape. If you are more than one voxel away from the surface, the density is just zero, so you have no way of knowing where the surface is. And, while one can reconstruct the surface position inside a voxel, you can't apply higher order filtering (which is applied over larger regions of space) to get a smoother surface reconstruction. Thus, voxel models tend to have a fair amount of aliasing, or require a lot of samples... ie. data. Distance fields are defined throughout space and they vary continuously across smooth surfaces (unlike voxel density which jumps from zero to one as soon as you cross into a shape). Thus, they can be sampled more sparsely and they can be reconstructed with less aliasing. Importantly, they can also be reconstructed relatively far away from the surface, as the reconstructed distance field gives you useful information such as what direction you should look to find the nearest surface and how far away it is (both useful for ray tracing or for estimating forces for interpenetrating objects). Which is why Frisken initially thought of using ADFs in collision detection over a decade ago. Also key for any good renderer is that when a sample is on the surface, the direction of the distance field is the same as the surface normal, which is useful. In the sculpting R&D test application above, Tomas Pettersson used a vector distance field for his sculpting system. This is an extension of ADFs, to do this you sample the vector distance instead of just the signed distance. The vector distance tells you both the direction to the closest surface and the distance from the surface. These are known as vector distance fields, and for the price of 3x as many values per sample, they allow The Foundry's team to reconstruct surface normals or direction vectors more accurately and they allow artists to represent non-manifold surfaces such as points, lines, and infinitely thin sheets. http://www.merl.com/publications/docs/TR2000-15.pdf Quote Link to comment Share on other sites More sharing options...

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