Non-rigid 3D shape retrieval using Multidimensional Scaling and Bag-of-Features

Matching non-rigid shapes is a challenging research field in content-based 3D object retrieval. In this paper, we present an image-based method to effectively address this problem. Multidimensional Scaling (MDS) and Principal Component Analysis (PCA) are first applied to each object to calculate its canonical form, which is afterward represented by 66 depth-buffer images captured on the vertices of an unit geodesic sphere. Then, each image is described as a word histogram obtained by the vector quantization of the image's salient local features. Finally, a multi-view shape matching scheme is carried out to measure the dissimilarity between two models. Experimental results on the McGill Articulated Shape Benchmark database demonstrate that, our method obtains better retrieval performance compared to the state-of-the-art.