Fisher encoding of differential fast point feature histograms for partial 3D object retrieval
Partial 3D object retrieval has attracted intense research efforts due to its potential for a wide range of applications, such as 3D object repair and predictive digitization. This work introduces a partial 3D object retrieval method, applicable on both point clouds and structured 3D models, which is based on a shape matching scheme combining local shape descriptors with their Fisher encodings. Experiments on the SHREC 2013 large scale benchmark dataset for partial object retrieval, as well as on the publicly available Hampson pottery dataset, demonstrate that the proposed method outperforms seven recently evaluated partial retrieval methods. HighlightsFirst application of Fisher encoding in 3D object retrieval.The dFPFH extends FPFH, capturing more accurately local geometric transitions.The proposed similarity addresses partiality.The proposed method outperforms state of the art in SHREC 2013 dataset.The proposed method outperforms state of the art in cultural heritage datasets.
Pattern Recognit.