Non-rigid 3D object retrieval using directional graph representation of wave kernel signature

Mirloo, Mahsa; Ebrahimnezhad, Hossein

doi:10.1007/s11042-017-4617-x

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…In this section, we evaluate the general performance of our proposed shape descriptor and compare it with several state-of-the-art spectral-based global shape descriptors including Shape-DNA [17], cShape-DNA [27], and GPS [20] algorithms. We chose these methods because they are widely used by researchers (e.g., [47][48][49]) to develop new descriptors or applications, or to evaluate the performance of their proposed descriptors. Moreover, cShape-DNA represents the normalized version of the original Shape-DNA.…”

Section: Retrieval Resultsmentioning

confidence: 99%

An Application of Manifold Learning in Global Shape Descriptors

et al. 2019

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With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific group of techniques in statistics and machine learning, known as manifold learning, to develop a global shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant descriptor. The extensive experiments performed in this study show that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace-Beltrami operator. The proposed method significantly outperforms state-of-the-art algorithms on several non-rigid shape retrieval benchmarks.

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