3D Model Retrieval Based on Depth Line Descriptor

In this paper, we propose a novel 2D/3D approach for 3D model matching and retrieving. Each model is represented by a set of depth lines which will be afterward transformed into sequences. The depth sequence information provides a more accurate description of 3D shape boundaries than using other 2D shape descriptors. Retrieval is performed when dynamic programming distance (DPD) is used to compare the depth line descriptors. The DPD leads to an accurate matching of sequences even in the presence of local shifting on the shape. Experimentally, we show absolute improvement in retrieval performance on the Princeton 3D Shape Benchmark database.