Shape2vec; Understanding 3D Shapes With AI
The paper “Shape2Vec: semantic-based descriptors for 3D shapes, sketches and images” is available here:
Code (coming soon according to the authors): https://github.com/ftasse/Shape2Vec
Shape2Vec; semantic-based descriptors for 3D shapes, sketches and images
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.