Heightfields are a nice addition to Houdini 16 for environment work. They more or less replicate the functionality of programs like Worldmachine. In today’s tutorial Manuel shows you how to create a terrain from scratch in Houdini and how to render it directly in Redshift3D, without baking out textures manually.
Branching growth is fascinating as it has a lot of hidden structure to it and is very intricate. Many methods have been proposed over the years to model branching structures, like trees. One algorithm that is particularly beautiful and simple is the “Space Colonization” algorithm, that Adam Runions proposed in 2007. It models branches by looking at their competition for space. The space that contains the branches is filled with points that serve as attractors […]
The past decade has seen a wealth of data visualization elements in UI design, movie sets and motion graphics. When it comes to creating these elements you’re left with two approaches: faking it vs. the real thing. In this quicktip we’re doing the real thing when it comes to flight data. We’re taking publicly available airport and route data and model the (usually) invisible lines that connect our world by air travel.
Quite some time ago I was trying to cook up something like the guys at moviebarcode.com: Some setups that’d deconstruct a given movie into its individual colors in a visually pleasing manner. Recently I thought it was time to try another attempt. Instead of linearly mapping the individual frames’ colors on a 2D plane, this time I wanted to create something that took advantage of 3D space.
When we saw Andy Lomas’ “Aggregation” series a few years back we were struck. How could you generate those intricate particle sculptures? The series’ title hinted at one possible solution: Diffusion limited aggregation or DLA. In this tutorial we’ll build a basic DLA setup using VEX and volumes. Also we’ll talk a bit about rendering our result in Mantra and Redshift.
This one is a rather brute force approach for a problem we had to solve on a recent job: Finding a path through a bunch of obstacles. Admittedly what we implement here isn’t a very sophisticated algorithm, but its power lies in its simplicity: It will find a path as long as it’s got enough points available.