Abstract: Sketch-based shape retrieval (SBSR) has become a hot research spot in the field of model
retrieval, pattern recognition, and computer vision. 3D deep representation based on convolutional
neural network (CNN) enables significant performance improvement over state-of-the-arts in task of
3D shape retrieval. Motivated by this, in this paper a sketch-based 3D model retrieval algorithm by
utilizing entropy representative views and CNN feature matching is proposed. The representative
views are obtained by viewpoint entropy. And the representative views are processed by edge
detection to get the contour image of 3D model projection. The CNN descriptors extracted as features
for representative view of each object. And the method of feature matching is based on CNN
descriptors. Our experiments on Shape Retrieval Contest (SHREC) 2012 database and SHREC 2013
database demonstrate that our method is better than state-of-the-art approaches.

Key words: 3D shape retrieval, convolutional neural network, representative view, entropy