Shape Similarity Search for Surfel-Based Models

Abstract

We present a 3D object retrieval system in which a surfel-based model serves as a query and similar objects are retrieved from a collection of surfel-based models. The system comprises a surfelization technique for converting polygonal mesh models into a corresponding surfel-based representation, pose normalization of surfel-based models, a depth buffer-based method for describing shape of surfel-based models, and a search engine. Our surfelization technique consists of an enhanced triangle rasterization procedure adapted on the original triangulated model geometric features. Surfel-based representation is normalized by applying a modification of the Principle Component Analysis to the set of surfels. The 3D-shape descriptor is extracted from the canonical coordinate frame of the surfel-based model using the orthographic depth images. Each image is used as the input for the 2D Fast Fourier Transform. Appropriate magnitudes of the obtained coefficients are used as components of the feature vector. The resulting feature vector possesses an embedded multi-resolution representation, is invariant with respect to similarity transforms and robust with respect to outliers. The retrieval effectiveness of the presented method for characterizing shape of surfel-based models is evaluated using precision-recall diagrams.