: We jointly embed shapes and images of three categories (chair, aeroplane and car) into a shared space. Distances between entities in the high-dimensional embedding space reflect object similarities between shapes and images (visualized by t-SNE here). Both 3D models and 2D images contain a wealth of information about everyday objects in our environment. However, it is difficult to semantically link together these two media forms, even when they feature identical or very similar objects. We propose a joint embedding space populated by both 3D shapes and 2D images of objects, where the distances between embedded entities reflect similarity between the underlying objects. This joint embedding space facilitates comparison between entities of either form, and allows for cross-modality retrieval. We construct the embedding space using 3D shape similarity measure, as 3D shapes are more pure and complete than their appearance in images, leading to more robust distance metrics. We then employ a Convolutional Neural Network (CNN) to "purify" images by muting distracting factors. The CNN is trained to map an image to a point in the embedding space, so that it is close to a point attributed to a 3D model of a similar object to the one depicted in the image. This purifying capability of the CNN is accomplished with the help of a large amount of training data consisting of images synthesized from 3D shapes. Our joint embedding allows cross-view image retrieval, image-based shape retrieval, as well as shape-based image retrieval. We evaluate our method on these retrieval tasks and show that it consistently outperforms state-of-the-art methods, and demonstrate the usability of a joint embedding in a number of additional applications.
Li et al. REF learned the joint embedding by associating generated images to the trained embeddings from shape images of objects.
Joint embeddings of shapes and images via CNN image purification
TOGS
Computer Science