Abstract:In this paper, a retrieval methodology for 3D articulated objects is presented that relies upon a graphbased object representation. The methodology is composed of a mesh segmentation stage which creates the Attributed Relation Graph (ARG) of the object along with a graph matching algorithm which matches two ARGs. The graph matching algorithm is based on the Earth Movers Distance (EMD) similarity measure calculated with a new ground distance assignment. The superior performance of the proposed retrieval methodo… Show more
“…Very often in the experimental comparisons, a partial 3D object retrieval method is shown to outperform a generic retrieval method, such as the methods of Vranic [73], Papadakis et al [60] and Johnson and Hebert [42]. However, it should be kept in mind that some state-of-the-art partial 3D object retrieval methods aim to address special object classes, as is the case with the method of Agathos et al [2], which focuses on the retrieval of articulated objects, and the methods of Lavoué [46] and Bronstein et al [10], which focus on the retrieval of non-rigid objects.…”
Section: Local Feature Histograms For Range Image Classificationmentioning
confidence: 98%
“…Agathos et al [2] proposed a graph-based representation method that decomposes objects using the mesh segmentation method previously introduced by some of the authors [3]. Geodesic extrema of an object are considered as salient points identified by means of the protrusion function, which depends on geodesic distances of all pairs of points in a neighborhood and reaches its local maxima at the tips of mesh protrusions.…”
Section: Retrieval Of 3d Articulated Objects Using a Graph-based Reprmentioning
This work offers an overview of the state-of-the-art on the emerging area of 3D object retrieval based on partial queries. This research area is associated with several application domains, including face recognition and digital libraries of cultural heritage objects. The existing partial 3D object retrieval methods can be mainly classified as: i) view-based, ii) partbased, iii) bag of visual words (BoVW)-based, and iv) hybrid methods combining these three main paradigms or methods which cannot be straightforwardly classified. Several methodological aspects are identified, including the use of interest points and the exploitation of 2.5D projections, whereas the available evaluation datasets and campaigns are addressed. A thorough discussion follows, identifying advantages and limitations.
“…Very often in the experimental comparisons, a partial 3D object retrieval method is shown to outperform a generic retrieval method, such as the methods of Vranic [73], Papadakis et al [60] and Johnson and Hebert [42]. However, it should be kept in mind that some state-of-the-art partial 3D object retrieval methods aim to address special object classes, as is the case with the method of Agathos et al [2], which focuses on the retrieval of articulated objects, and the methods of Lavoué [46] and Bronstein et al [10], which focus on the retrieval of non-rigid objects.…”
Section: Local Feature Histograms For Range Image Classificationmentioning
confidence: 98%
“…Agathos et al [2] proposed a graph-based representation method that decomposes objects using the mesh segmentation method previously introduced by some of the authors [3]. Geodesic extrema of an object are considered as salient points identified by means of the protrusion function, which depends on geodesic distances of all pairs of points in a neighborhood and reaches its local maxima at the tips of mesh protrusions.…”
Section: Retrieval Of 3d Articulated Objects Using a Graph-based Reprmentioning
This work offers an overview of the state-of-the-art on the emerging area of 3D object retrieval based on partial queries. This research area is associated with several application domains, including face recognition and digital libraries of cultural heritage objects. The existing partial 3D object retrieval methods can be mainly classified as: i) view-based, ii) partbased, iii) bag of visual words (BoVW)-based, and iv) hybrid methods combining these three main paradigms or methods which cannot be straightforwardly classified. Several methodological aspects are identified, including the use of interest points and the exploitation of 2.5D projections, whereas the available evaluation datasets and campaigns are addressed. A thorough discussion follows, identifying advantages and limitations.
“…Results on McGill dataset: In Table 1, we compare the different evaluation results of our method as well as five state-of-the-art methods, including the Covariance Method (CM) [4], Graph-based method [8], PCA-based VLAT (PVLAT) method [10], Hybrid 2D/3D method [35] and Hybrid BoW method [3]. The evaluation results tell us that SA is better than SG and HA for the multiscale descriptor (e.g.…”
Section: Retrieval Based On Mscmentioning
confidence: 99%
“…Smeets et al [7,1] used the spectra of the geodesic distance matrix as shape feature (called SD-GDM). To perform partial shape matching and recognition, many of the researchers turned to the local shape descriptors, like the segmentation based method [8,9], the depth image based method (BF-DSIFT-E [1], PCA-based VLAT [10]). Local descriptors based on diffusion geometry also presented promising properties (e.g.…”
“…Table 2 summarizes the retrieval performance of the proposed method on the McGill dataset. We compare our results to various state-of-the-art methods: the Hybrid BoW [20], the PCA-based VLAT approach [35], the graphbased approach of Agathos et al [1], and the hybrid 2D/3D approach of Papadakis et al [27]. Although the proposed method does not consider structural information of shapes, it achieves the best performance on NN and DCG.…”
Several descriptors have been proposed in the past for 3D shape analysis, yet none of them achieves best performance on all shape classes. In this paper we propose a novel method for 3D shape analysis using the covariance matrices of the descriptors rather than the descriptors themselves. Covariance matrices enable efficient fusion of different types of features and modalities. They capture, using the same representation, not only the geometric and the spatial properties of a shape region but also the correlation of these properties within the region. Covariance matrices, however, lie on the manifold of Symmetric Positive Definite (SPD) tensors, a special type of Riemannian manifolds, which makes comparison and clustering of such matrices challenging. In this paper we study covariance matrices in their native space and make use of geodesic distances on the manifold as a dissimilarity measure. We demonstrate the performance of this metric on 3D face matching and recognition tasks. We then generalize the Bag of Features paradigm, originally designed in Euclidean spaces, to the Riemannian manifold of SPD matrices. We propose a new clustering procedure that takes into account the geometry of the Riemannian manifold. We evaluate the performance of the proposed Bag of Covariance Matrices framework on 3D shape matching and retrieval applications and demonstrate its superiority compared to descriptor-based techniques.
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