Shape Similarity System driven by Digital Elevation Models for Non-Rigid Shape Retrieval

Shape similarity computation is the main functionality for shape matching and shape retrieval systems. Existing shape similarity frameworks proceed by parameterizing shapes through the use of global and/or local representations computed in the 3D or 2D space. Up to now, global methods have demonstrated their rapidity, while local approaches offer slower, but more accurate solutions. This paper presents a shape similarity system driven by a global descriptor encoded as a Digital Elevation Model (DEM) associated to the input mesh. The DEM descriptor is obtained through the jointly use of a mesh flattening technique and a 2D panoramic projection. Experimental results on the TOSCA public dataset and a comparison with state-of-the-art methods illustrate the effectiveness of the proposed method in terms of accuracy and efficiency.

Dr. Daniela Craciun has presented the paper during the 3DOR workshop of the Eurographics 2017 conference on April 24th at Lyon, France

The article is available on the OpenAIRE section of the Zenodo Platform