Deformable Model Retrieval Based on Topological and Geometric Signatures

Abstract

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-With the increasing popularity of 3D applications such as computer games, a lot of 3D geometry models are being created.To encourage sharing and reuse, techniques that support matching and retrieval of these models are emerging. However, only a few of them can handle deformable models, that is, models of different poses, and these methods are generally very slow. In this paper, we present a novel method for efficient matching and retrieval of 3D deformable models. Our research idea stresses using both topological and geometric features at the same time. First, we propose Topological Point Ring (TPR) analysis to locate reliable topological points and rings. Second, we capture both local and global geometric information to characterize each of these topological features. To compare the similarity of two models, we adapt the Earth Mover Distance (EMD) as the distance function and construct an indexing tree to accelerate the retrieval process. We demonstrate the performance of the new method, both in terms of accuracy and speed, through a large number of experiments.

A new convexity measurement for 3D meshes

“…Wang et al [31] proposed to compare non-rigid 3D models based on a local feature named Intrinsic Spin Images (ISIs), which is designed by generalizing the traditional spin images [7] from 3D space to N-dimensional intrinsic shape space. Tam and Lau [29] used topological and geometric features simultaneously to search deformable 3D models. Mahmoudi and Sapiro [13] designed six isometric-invariant signatures by using the distributions of intrinsic distances.…”

Embedding Retrieval of Articulated Geometry Models

“…on defining distinctive geometric features [1], [2]. The vast amount of features (over many hundreds in dimension) have created two problems.…”

“…Other methods use bag-based matching techniques like bipartite matching or Earth Mover Distance [18] to avoid graph matching. These methods include geodesic histogram [19], curvature, area and thickness histogram [2], and stretching histogram [20].…”

“…This scheme, however, still suffers if the database contains a lot of similar-skeleton models (e.g., dog, wolf, lion), as the first step cannot prune these models by structures, which leads to high computational cost in the second step. In [2], we have defined a metric measure and used a distance-based indexing technique for fast k-nearest neighbor search. This method works when k (the number of retrieved models) is small.…”

IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

“…For model-based retrieval, shape similarities are measured by using various geometric shape descriptors including shape distribution (Assfalg et al, 2007;Akgul et al, 2009), spherical harmonic function (Chen et al, 2014;Mademlis et al, 2009), shape topology (Tam and Lau, 2007), shape spectral (Jain and hang, 2007), and radon transform (Daras et al, 2006). In the topology-based method (Tam and Lau, 2007), model topologies are represented as skeletons/graphs. The methods rely on the fact that the skeleton is a compact shape descriptor, and assume that similar shapes have similar skeletons.…”