Fill arbitrary 2D shape with given set of rectangles
I have a set of rectangles and arbitrary shape in 2D space. The shape is not necessary a polygon (it may be a circle), and rectangles have different widths and heights. The task is to approximate the shape with rectangles as close as possible. I can't change rectangles dimensions, but rotation is permitted.
It sounds very similar to packing problem and covering problem but covering area is not rectangular...
I guess it's NP problem, and I'm pretty sure there should be some papers that show good heuristics to solve it, but I don't know what to google? Where should I start?
Update: One idea just came into my mind but I'm not sure if it's worth investigating. What if we consider bounding shape as a physical mold filled with water. Each rectangle is considered as a positively charged particle with size. Now drop the smallest rectangle to it. Then drop the next by size at random point. If rectangles too close they repel each other. Keep adding rectangles until all are used. Could this method work?
Answer
I think you could look for packing and automatic layout generation algorithms. Automatic VLSI layout generation algorithms might need similar things, just like textile layout questions...
This paper Hegedüs: Algorithms for covering polygons by rectangles seems to address a similar problem. And since this paper is from 1982, it might be interesting to look at the papers which cite this one. Additionally, this meeting seems to be discussing research problems related to this, so might be a starting point for keywords or names who do research in this idea.
I don't know if the computational geometry research has algorithms for your specific problem, or if these algorithms are easy/practical enough to implement. Here is how I would approach it if I had to do it without being able to look up previous work. This is just a direction, by far not a solution...
Formulate it as an optimization problem. You have discrete variables of which rectangles you choose (yes or no) and continuous variables (location and orientation of the triangles). Now you can set up two independent optimizations: a discrete optimization which picks the rectangles; and a continuous that optimizes for the location and orientation once rectangles are given. Interleave these two optimizations. Of course the difficulty lies in the formulation of optimizations, and designing your error energy such that it does not get stuck in some strange configurations (local minima). I'd try to get the continuous as a least squares problem such that I can use standard optimizations libraries.