Learning a discriminative deformation-invariant 3D shape descriptor via many-to-one encoder

Recent advances in 3D acquisition techniques have led to a rapid increase in the size of database of three dimensional (3D) models across areas as diverse as engineering, medicine and biology, etc. Therefore, developing an efficient shape retrieval method has been attracting more and more attention in recent years. In this paper, we have developed a novel learning paradigm for extracting a concise data-driven shape descriptor to address challenging issues posed by structural deformation variations and noise present in 3D models. First, we use the scale invariant heat kernel signature (SIHKS) to describe the vertex of the shape. The locality-constrained linear coding (LLC) is employed to encode each vertex of the shape to form the global shape representation. Then we develop a discriminative shape descriptor for retrieval using many-to-one encoder. Our proposed shape descriptor is extensively evaluated on three well-known benchmark datasets including McGill, SHREC’10 ShapeGoogle and SHREC’14 human. Experimental results on 3D shape retrieval demonstrate the superior performance of our proposed method over the state-of-the-art methods and is robust to large deformations.