This is one of those projects that seems much cooler to the maker than the observer. Here I have a biplane. What makes it special is that it was rendered using Carson Fenimore’s 3D renderer. If you have never heard of it, that’s ok – that probably won’t change, even after I finalize things with Pixar. It will probably remain anonymous, just as my stock and pay on the deal will be kept secret, leaving me superficially the same, as always, a humble man unwilling to brag.

This renderer was implemented entirely in c++ under Mac OS X and makes use of no external libraries, other than the STL for data containers and OpenGL for drawing 2D points (really 3D points with a Z value of 0). This means this renderer does the following:

• Polygon rasterization – quads and triangles
• Z Buffering – using patented hidden secret technology
• Geometric transformations (scale, translate, rotate)
• Arbitrary view (camera can be anywhere; specify view angle along with up, look at, and look from vectors
• Phong shading

The lesson I learned from this is: Don’t be deceived: simple things are often not easy to do. This was not easy to implement – there’s no “cookie-cutter” way to do your own renderer, especially if you have a weak background in linear algebra.

Biplane – in all its magesty

Apple – Notice the Shiny spot thanks to Mr. Phong

Bike

General – Very large and complex model

This is a new classic in my book: Combine stir fried chicken on a well boiled set of potatoes. Scrum-didily-upmtious!

A the Hough, now there’s an amazingly simple method for finding circles, lines, and other shapes. Lest this sounds too narrow an application, consider the wild possibility of finding a pool ball. The hough can find these in almost linear time. Amazing!

The idea is this: build an “accumulator”, which is really a voting array for parameters describing the object you wish to find. In the case of circles, you could have one accumulator for each approximate radius. Your task is then to go through the source image and find features which “might” be parts of a pool ball; in this case, edges might work. For each point on an edge, we “vote” in a radius around the point. If we do this for all points around a ball, the locus of the ball will have a high number of votes.

Here’s an example image with some pictures of the accumulator for circles of radius 32 and 48.

Source Image:

Paramater Space at radius 48

Paramater Space at radius 32

Shown below, is the final result of my Hough Transform for radius 32. Note that it missed one, but had no false positives. Not bad, especially considering I use a general approach that isn’t “hand tuned” to this image.