We propose a 3D sub-query expansion approach for boosting sketch-based multi-view image retrieval. The core idea of our method is to automatically convert two (guided) 2D sketches into an approximated 3D sketch model, and then generate multi-view sketches as expanded sub-queries to improve the retrieval performance. To learn the weights among synthesized views (sub-queries), we present a new multi-query feature to model the similarity between sub-queries and dataset images, and formulate it into a convex optimization problem. Our approach shows superior performance compared with the state-of-the-art approach on a public multi-view image dataset. Moreover, we also conduct sensitivity tests to analyze the parameters of our approach based on the gathered user sketches.