There has been much interest in generating 3D shapes that are perceived to be ``creative'' and previous works develop tools that can be used to create shapes that may be considered ``creative''. However, previous research either do not formally define what is a creative shape, or describe manually pre-defined methods or formulas to evaluate whether a shape is creative. In this paper, we develop a computational measure of 3D shape creativity by learning with raw data and without any pre-defined conception of creativity. We first collect various types of data on the human perception of 3D shape creativity. We then analyze the data to gain insights on what makes a shape creative, show results of our learned measure, and discuss some applications.