Enhancing online 3D products through crowdsourcing

Abstract

In this paper, we propose to build semantic links between a product's textual description and its corresponding 3D visualization. These links help gathering knowledge about a product and ease browsing its 3D model. Our goal is to support the common behavior that when reading a textual information of a product, users naturally imagine how it looks like in real life. We generate the association between a textual description and a 3D feature from crowdsourcing. A user study of 82 people assesses the usefulness of the association for subsequent users, both for correctness and efficiency. Users are asked to perform the identification of features on 3D models; from the traces, associations leading to recommended views are derived. This information (recommended view) is proposed to subsequent users for performing the same task. Whereas the associations could be simply given by an expert, crowdsourcing offers advantages: we have inexpensive experts in the crowd as well as a natural access to users' (eg. customers') preferences and opinions.