In this paper we present a method for geometry synthesis by example, inspired by techniques from texture synthesis. Given an example of input geometry, we synthesize new output geometry that is perceived similar to the input geometry, but at the same time differs in its local appearance. We assume the input geometry satisfies the constraints of a Markov Random Field model, and represent the input geometry by a hierarchical distance field. This allows us to perform fast matching between a target distance field that is partially synthesized, and the input distance field. Once the target distance field is completed, we copy the original corresponding geometry elements to the synthesized result. We show that automatically generating geometry by example can be achieved within reasonable computing times, and is able to produce convincing results.