The perception of shapes and images are important areas that have been explored in previous research. While the problem of the interestingness of images has been explored, there is no previous work in the interestingness of 3D shapes to the best of our knowledge. In this paper, we study this novel problem. We collect data on how humans perceive the interestingness of 3D shapes and analyze the data to gain insights on what makes a shape interesting. We show that a function can be learned to autonomously predict an interestingness score given a new shape and demonstrate some interestingness-guided 3D shape applications.