Embedding Retrieval of Articulated Geometry Models

Tam, Gary K. L.; Lau, Rynson W. H.

doi:10.1109/tpami.2012.17

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…Bronstein et al [28] directly compute the distance between two non-rigid shapes by formulating the Gromov-Hausdorff distance as a multidimensionalscaling-like continuous optimisation problem. Tam and Lau [29] showed that manifold learning can be used to improve the shape retrieval accuracy of existing methods.…”

Section: Related Workmentioning

confidence: 99%

Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval

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Sun

Rosin

et al. 2015

Pattern Recognition

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Retrieval of 3D shapes is a challenging problem, especially for non-rigid shapes.One approach giving favourable results uses multidimensional scaling (MDS) to compute a canonical form for each mesh, after which rigid shape matching can be applied. However, a drawback of this method is that it requires geodesic distances to be computed between all pairs of mesh vertices. Due to the superquadratic computational complexity, canonical forms can only be computed for low-resolution meshes. We suggest a linear time complexity method for computing a canonical form, using Euclidean distances between pairs of a small subset of vertices. This approach has comparable retrieval accuracy but lower time complexity than using global geodesic distances, allowing it to be used on higher resolution meshes, or for more meshes to be considered within a time budget.

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Section: Related Workmentioning

confidence: 99%

Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval

Pickup

Sun

Rosin

et al. 2015

Pattern Recognition

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“…A number of recent works have successfully applied this framework for various purposes in mesh processing and analysis, including [Sun et al 2009;Ovsjanikov et al 2010;Mémoli 2011] who study the kernel arising from a converging mesh Laplacian operator on a surface, leading to the definition of diffusion distances on a mesh and an isometric one-point matching technique. It has also been used to determine symmetry , for co-segmentation [Sidi et al 2011] and for retrieval [Tam and Lau 2012].…”

Section: Spectral Analysismentioning

confidence: 99%

Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry

Tam¹,

Martin

Rosin

et al. 2014

ACM Trans. Graph.

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Finding correspondences between two surfaces is a fundamental operation in various applications in computer graphics and related fields. Candidate correspondences can be found by matching local signatures, but as they only consider local geometry, many are globally inconsistent. We provide a novel algorithm to prune a set of candidate correspondences to those most likely to be globally consistent. Our approach can handle articulated surfaces, and ones related by a deformation which is globally non-isometric, provided that the deformation is locally approximately isometric. Our approach uses an efficient diffusion framework, and only requires geodesic distance calculations in small neighbourhoods, unlike many existing techniques which require computation of global geodesic distances. We demonstrate that, for typical examples, our approach provides significant improvements in accuracy, yet also reduces time and memory costs by a factor of several hundred compared to existing pruning techniques. Our method is furthermore insensitive to holes, unlike many other methods.

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“…There are also feature descriptors for shape matching in articulated (or non‐rigid) model retrieval and in finding similar parts of models . Gal and Cohen‐Or used salient geometric features for partial shape matching and similarity.…”

Section: Related Workmentioning

confidence: 99%

Fast accessing Web3D contents using lightweight progressive meshes

Wen

Xie

Jia

2015

Computer Animation & Virtual

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Accessing Web3D contents is relatively slow through Internet under limited bandwidth. Preprocessing of 3D models can certainly alleviate the problem, such as 3D compression and progressive meshes (PM). But none of them considers the similarity between components of a 3D model, so that we could take advantage of this to further improve the efficiency. This paper proposes a similarity-aware data reduction method together with PM, called lightweight progressive meshes (LPM). LPM aims to excavate similar components in a 3D model, generates PM representation of each component left after removing redundant components, and organizes all the processed data using a structure called lightweight scene graph. The proposed LPM possesses four significant advantages. First, it can minimize the file size of 3D model dramatically without almost any precision loss. Because of this, minimal data is delivered. Second, PM enables the delivery to be progressive, so called streaming. Third, when rendering at client side, due to lightweight scene graph, decompression is not necessary and instanced rendering is fully exerted. Fourth, it is extremely efficient and effective under very limited bandwidth, especially when delivering large 3D scenes. Performance on real data justifies the effectiveness of our LPM, which improves the state-of-the-art in accessing Web3D contents.

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Embedding Retrieval of Articulated Geometry Models

Cited by 6 publications

References 31 publications

Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval

Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval

Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry

Fast accessing Web3D contents using lightweight progressive meshes

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