![]() The resolution of the 3D grid is determined from the Leaf Count or Azimuth/Altitude Band values. The canopy is then creating by determining the iso-surface at an appropriate threshold value. Generates a 3D volumetric grid based on the current canopy radius and height, and then uses a Perlin noise function to generate values in each cell. See Geodesic polyhedron for more information on geodesic subdivision. Geodesic subdivision occurs in incremental steps, so you may need to change the Leaf Count value a lot to see any changes. This option divides the sphere into eight spherical triangles, then iteratively subdivides each triangle based on the square root of the Leaf Count or the given Geodesic Segmentation value. See Generating Equidistant Points on a Sphere for more information on the Fibonacci spiral. This is a surprisingly simple method, but seems to give the best approximation of equal-area subdivision than any other method I have found, and accurately represents the Leaf Count. This uses a Fibonacci spiral or lattice to distribute points from the apex down over the surface of a sphere. This option divides a sphere into roughly equal-area patches based on the square root of Leaf Count or Azimuth/Altitude Band values, and then randomly locates a point within each patch. The total number of random points is controlled by the Leaf Count value. This option generates a completely random set of azimuth and altitude values, except for the first 5 points which are distributed pseudo-randomly to form a basic diamond between base and apex to ensure a reasonable shape even with only a very small number of points. ![]() If no subdivisions are selected, the large flat facetted surfaces will be edged with sketchy lines and the Bushiness value will instead generate additional leaves along the edges. This means that leaf size and density is stepped incrementally, so you may need to change the Leaf Count value a lot to see any changes. As the subdivision is recursive, the total number of leaves can quickly blow out so the subdivisions are dynamically limited by face size and number. This option allows you to select from a range of polyhedra based on Platonic and Archimedean Solids, and then subdivide their surfaces into triangular leaves. Figure 4: The various point/surface distribution methods. Alternatively, you can also use various spatial algorithms to calculate values over a 3D volumetric data grid and then generate the canopy as an iso-surface. Another is to distribute random, semi-random and ordered points over a sphere and then connect those points into facets to form a surface. One is to use some standard polyhedra and subdivide their surfaces into triangular leaves. There are a number of ways you can generate abstract but natural-looking geometric canopy shapes. However, it is probably worth explaining some aspects of the various geometric representations. Each procedural generator requires a fair number of controls for the various parameters that govern the results, so simple experimentation is the only real way to gain an understanding of what each one does. Thus, the hope is that having tools that make modelling and describing it relatively simple and straightforward may motivate more analysis tools to better support the dynamic shading effects of deciduous vegetation.įigure 3b: The seasonal variation settings dialog box.Īs shown in Figure 2 above, the app can generate both simple abstract geometric representations as well as much more realistic trees that use mapped textures to model the foliage (thanks to proctree.js - see below). Unfortunately there are very few tools available that can viably model or describe these dynamic seasonal shading effects, which in turn means that most building analysis software either ignore it or provide only limited means to deal with it. ![]() Figure 2: The types of low-poly procedural tree you can generate. Also, the leaf canopy needs to be able to vary its colour and opacity across the year to properly represent the dynamic seasonal effects of deciduous trees that shed and then regrow their leaves annually. To be used effectively in analysis, tree geometry needs to be as simple and light-weight as possible, but still sufficiently configurable to reasonably represent the size, shape and shading effects of existing or likely trees on a site. ![]() The aim of this app is to generate reasonably configurable low-polygon parametric / procedural trees for use with BIM and building performance analysis models.
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