Isometric Deformation Modelling for Object Recognition

Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval

“…We used the well-known evaluation tool: the Precision Recall plot. We plotted the Precision Recall graph for the whole database in comparison with the best results run with methods involved in the benchmark (Ohbuchi [42] (MR-BF-DSIFT-E), Smeets [43] (DMEVD), and Wuhrer [44] (CF)). Fig.…”

Persistence-Based Structural Recognition

“…We also compare with the state-of-the-art method, including ShapeDNA [35] and DM-EVD [38], which show the best performance in a larger contest [29]. We test the ShapeDNA method on the the same simplified meshes, and take the result of DM-EVD and other methods from [28], in which DM-EVD is performed on the fine meshes.…”

Retrieval and classification methods for textured 3D models: a comparative study

“…To deal with non-rigid deformations (bendings) it is necessary to adopt shape descriptions that are invariant to isometric shape deformations. A suitable metric for comparing non-rigid shapes is the geodesic one; indeed 3D shape descriptions based on geodesics, such as geodesic distance matrices [68] or geodesic skeleton paths [38], have been successfully adopted for non-rigid shape comparison, see also [44]. In addition to geodesic, more sophisticated choices are possible, such as the diffusion or the commute-time distance [77].…”

“…Let G be a n × n geodesic distance matrix, where n is the number of vertices in S and the element G(i, j) denotes the geodesic distance from the vertex i to vertex j on S. Building on G, the centralised geodesic matrix [50] is defined as D = G − 1 n G − G1 n +1 n G1 n , where 1 n denotes a n×n matrix having each component equal to 1/n. Following [68], a spectral representation of the geodesic distance is finally adopted as shape descriptor, that is, a vector of eigenvalues…”