Comparative Study of Multi-View 3D Object Retrieval with Autoencoder & Deep Embedding Network

In many computer vision based problems, multiview 3D object retrieval is very useful with many application possibilities. Actually multiview 3D object is represented by a set of different views of 2D images. There are many hand-crafted features extraction techniques. Rather than using them, deep embedding network and autoencoder are used to extract features and calculate Euclidean distance to measure the similarity. This paper emphasizes on the process of retrieving 3D object from multi-view 2D images. Two established deep learning based solutions are used to retrieve images of multi-view 3D object. Finally different evaluation metrics are used to compare image retrieval performance accuracy & compare computation time and space complexity for both autoencoder and deep embedding network techniques. Here, dimension reduction algorithms PCA & t-SNE are also used to interpret the retrieval results. The experimented results show that deep embedding network gained 98% accuracy & autoencoder gained 97% accuracy to retrieve multi-view 2D images using RGB-D dataset.