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Understanding the SDF - Signed Distance Field (part 1/3)

PART 1: Understanding how an sdf works, sampling the sdf value and the gradient of the sdf. (this video) PART 2: Example 1 - sampling and displacing the sdf using the gradient of the sdf.    • SDF example 1: sampling and displacin...   PART 3: Example 2 - snapping particles to the surface of an object by using and sdf as a data acceleration structure.    • SDF example 2: snapping particles to ...   Add me on Instagram: pclaesvfx Hipfile: http://bit.ly/sdf_hipfile OVERVIEW: Covering the theory on how an sdf (signed distance field) works. A point animates through the active voxels of a vdb that has computed the sdf of the pigshead test geometry. As the point moves through the voxels, both the sdf value as well as the gradient vector are sampled and displayed numerically as well a geometrically. FIRST: We are dealing with a field, which means we are dealing with voxels and volumetric data. What is being stored is the distance to the surface. The isocontour represents a slice of the pigshead test geometry. SECOND: When we are outside of the active region, the sdf value looks up the closest voxel value. We are working with a vdb which has an active region made up out of an exterior and an interior band. As soon as the point goes inside the active region of the vdb, it will correctly start sampling the values. THIRD: We are dealing with the signed distance. By convention it is agreed that when we are sampling the distance value on the inside region of the object, it will have a negative sign. When we sample the outside it will be a positive value and when we are sampling values on the surface, it will be zero. FOURTH: The region where we are on the surface represents the isocontour where the values are zero. When we are on the surface, the distance to the surface is zero. FIFTH: The gradient of the sdf is a direction vector that is not normalized and represents the direction from a low value to a higher neighboring value. We can combine the normalized negated gradient vector with the sampled sdf value to get the displacement vector to get from a point in the volume to the closest point on the surface. SIXTH: We can compute the sdf using a 'vdbfrompolygon' object, this turns the polygon object into an sdf volume. We can display the primitive hulls in the viewport to see the active region of the voxels. Turning on 'Fill Interior' will compute the sdf values for all voxels on the inside of the geometry. SEVENTH: We can sample the sdf value with a single point inside an attribute vop. Inside the attribute vop we can use the 'volume sample' and 'volume gradient'. The sampled sdf value will be a single scalar value that represents the signed distance to the surface. The sampled gradient will be a vector value that represents the direction from a low value to a high value. EIGHT: The goal of using the sdf is to be able to quickly look up the distance to the surface instead of computing it. The vdbfrompolygons will already have precomputed it. NINTH: We can get to the surface by taking the normalized gradient vector, negating it and multiplying it by the sdf (scalar) value. TENTH: We can slice an intersection of the pigshead by performing a boolean operation (seam) between a grid and the pigshead geometry. This is great for creating slices or outlines. ELEVENTH: Use the visualize tree to create boxes for each active voxel. This is very easy to 'voxelize' an object. After assembling and packing the boxes into packed primitives, we can select a slice of boxes using a bound and a group by bounding object. TWELFTH: We can use the points representing the centroids of the voxel-boxes and each point will sample the sdf value at its location. The points with a positive value will get a '+' (plus sign) copied onto them and the points with a negative value will get a '-' (minus sign) copied onto them. The resampled seam curve of the pigshead will get '0' (zero) copied onto them. For business or one-on-one visual effects consulting inquiries, you can reach me at [email protected]

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