Shape2Vec

Convolutional neural networks have been successfully used to compute shape descriptors, or jointly embed shapes and sketches in a common vector space. We propose a novel approach that leverages both labeled 3D shapes and semantic information contained in the labels, to generate semantically-meaningful shape descriptors. A neural network is trained to generate shape descriptors that lie close to a vector representation of the shape class, given a vector space of words. This method is easily extendable to range scans, hand-drawn sketches and images. This makes cross-modal retrieval possible, without a need to design different methods depending on the query type. We show that sketch-based shape retrieval using semantic-based descriptors outperforms the state-of-the-art by large margins, and mesh-based retrieval generates results of higher relevance to the query, than current deep shape descriptors.