Contour-based 3D Modeling through Joint Embedding of Shapes and Contours

In this paper, we propose a novel space that jointly embeds both 2D occluding contours and 3D shapes via a variational autoencoder (VAE) and a volumetric autoencoder. Given a dataset of 3D shapes, we extract their occluding contours via projections from random views and use the occluding contours to train the VAE. Then, the obtained continuous embedding space, where each point is a latent vector that represents an occluding contour, can be used to measure the similarity between occluding contours. After that, the volumetric autoencoder is trained to first map 3D shapes onto the embedding space through a supervised learning process and then decode the merged latent vectors of three occluding contours (from three different views) of a 3D shape to its 3D voxel representation. We conduct various experiments and comparisons to demonstrate the usefulness and effectiveness of our method for sketch-based 3D modeling and shape manipulation applications.