Tess

verb.eval.Tess

CLASS

Tess contains static, immutable algorithms for tessellation of NURBS curves and sufaces. Tessellation is the decomposition of the analytical NURBS representation into discrete meshes or polylines that are useful for drawing.

Some of these algorithms are "adaptive" - using certain heuristics to sample geometry where such samples make sense - while others are "regular" in that they sample regularly throughout a parametric domain. There are tradeoffs here. While adaptive algorithms can sometimes yield "better" results that are smaller or more economical, this can sometimes come at increased computational cost. For example, it is sometimes necessarily to compute higher order derivatives in order to obtain these more economical results. Your usage of these algorithms should consider these tradeoffs.

rationalCurveRegularSample

STATIC METHOD

rationalCurveRegularSample(curve : NurbsCurveData, numSamples : Int, includeU : Bool) : Array<Point>

Sample a NURBS curve at equally spaced parametric intervals

params

NurbsCurveData object
integer number of samples
whether to prefix the point with the parameter

returns

an array of points, prepended by the point param if required

rationalCurveRegularSampleRange

STATIC METHOD

rationalCurveRegularSampleRange(curve : NurbsCurveData, start : Float, end : Float, numSamples : Int, includeU : Bool) : Array<Point>

Sample a range of a NURBS curve at equally spaced parametric intervals

params

NurbsCurveData object
start parameter for sampling
end parameter for sampling
integer number of samples
whether to prefix the point with the parameter

returns

an dictionary of parameter - point pairs

rationalCurveAdaptiveSample

STATIC METHOD

rationalCurveAdaptiveSample(curve : NurbsCurveData, tol : Float, e-6 : undefined, includeU : Bool) : Array<Point>

Sample a NURBS curve over its entire domain, corresponds to this algorithm

params

NurbsCurveData object
tol for the adaptive scheme
whether to prefix the point with the parameter

returns

an array of dim + 1 length where the first element is the param where it was sampled and the remaining the pt

rationalCurveAdaptiveSampleRange

STATIC METHOD

rationalCurveAdaptiveSampleRange(curve : NurbsCurveData, start : undefined, end : undefined, tol : undefined, includeU : undefined) : Array<Point>

Sample a NURBS curve at 3 points, facilitating adaptive sampling

params

NurbsCurveData object
start parameter for sampling
end parameter for sampling
whether to prefix the point with the parameter

returns

an array of dim + 1 length where the first element is the param where it was sampled and the remaining the pt

rationalSurfaceNaive

STATIC METHOD

rationalSurfaceNaive(surface : NurbsSurfaceData, divs_u : Int, divs_v : Int) : MeshData

Tessellate a NURBS surface on equal spaced intervals in the parametric domain

params

NurbsSurfaceData object
number of divisions in the u direction
number of divisions in the v direction

returns

MeshData object

divideRationalSurfaceAdaptive

STATIC METHOD

divideRationalSurfaceAdaptive(surface : NurbsSurfaceData, options : AdaptiveRefinementOptions) : Array<AdaptiveRefinementNode>

Divide a NURBS surface int equal spaced intervals in the parametric domain as AdaptiveRefinementNodes

params

NurbsSurfaceData object
SurfaceDivideOptions object

returns

MeshData object

rationalSurfaceAdaptive

STATIC METHOD

rationalSurfaceAdaptive(surface : NurbsSurfaceData, options : AdaptiveRefinementOptions) : MeshData

AdaptiveRefinementOptions

verb.eval.AdaptiveRefinementOptions

CLASS

constructor

METHOD

new AdaptiveRefinementOptions()

normTol

PROPERTY

normTol : Float

minDepth

PROPERTY

minDepth : Int

maxDepth

PROPERTY

maxDepth : Int

refine

PROPERTY

refine : Bool

minDivsU

PROPERTY

minDivsU : Int

minDivsV

PROPERTY

minDivsV : Int

AdaptiveRefinementNode

verb.eval.AdaptiveRefinementNode

CLASS

Structure of the child nodes
in the adaptive refinement tree

  v
  ^
  |
  +--> u

                        neighbors[2]

                (u0,v1)---(u05,v1)---(u1,v1)
                  |           |          |
                  |     3     |     2    |
                  |           |          |
neighbors[3]   (u0,v05)--(u05,v05)--(u1,v05)   neighbors[1]
                  |           |          |
                  |     0     |     1    |
                  |           |          |
                (u0,v0)---(u05,v0)---(u1,v0)

                        neighbors[0]

constructor

METHOD

new AdaptiveRefinementNode(srf : NurbsSurfaceData, corners : Array<SurfacePoint>, neighbors : Array<AdaptiveRefinementNode>)

isLeaf

METHOD

isLeaf()

center

METHOD

center()

evalCorners

METHOD

evalCorners()

evalSrf

METHOD

evalSrf(u : Float, v : Float, srfPt : SurfacePoint) : SurfacePoint

getEdgeCorners

METHOD

getEdgeCorners(edgeIndex : Int) : Array<SurfacePoint>

getAllCorners

METHOD

getAllCorners(edgeIndex : Int) : Array<SurfacePoint>

midpoint

METHOD

midpoint(index : undefined)

hasBadNormals

METHOD

hasBadNormals() : Bool

fixNormals

METHOD

fixNormals() : Void

shouldDivide

METHOD

shouldDivide(options : AdaptiveRefinementOptions, currentDepth : Int)

divide

METHOD

divide(options : AdaptiveRefinementOptions) : Void

triangulate

METHOD

triangulate(mesh : MeshData) : MeshData

triangulateLeaf

METHOD

triangulateLeaf(mesh : MeshData) : MeshData

neighbors

PROPERTY

neighbors : Array<AdaptiveRefinementNode>