3D Shape correspondence by isometry-driven greedy optimization

Sahillioğlu, Yusuf; Yemez, Y.

doi:10.1109/cvpr.2010.5540178

Cited by 36 publications

(24 citation statements)

References 19 publications

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“…Probabilistic inference with points' topology is used to find point correspondences for 2D non-rigid points [17,18] and 3D dense surface points [19,20]. Different from them, we propose the soft graph matching model with discrete combinatorial optimization algorithm to find 3D sparse marker correspondences in online manner by solving a problem.…”

Section: Point Correspondencementioning

confidence: 99%

Toward accurate real-time marker labeling for live optical motion capture

Xia

Fei

et al. 2017

Vis Comput

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Marker labeling plays an important role in optical motion capture pipeline especially in real-time applications; however, the accuracy of online marker labeling is still unclear. This paper presents a novel accurate real-time online marker labeling algorithm for simultaneously dealing with missing and ghost markers. We first introduce a soft graph matching model that automatically labels the markers by using Hungarian algorithm for finding the global optimal matching. The key idea is to formulate the problem in a combinatorial optimization framework. The objective function minimizes the matching cost, which simultaneously measures the difference of markers in the model and data graphs as well as their local geometrical structures consisting of edge constraints. To achieve high subsequent marker labeling accuracy, which may be influenced by limb occlusions or self-occlusions, we also propose an online high-quality fullbody pose reconstruction process to estimate the positions of missing markers. We demonstrate the power of our approach by capturing a wide range of human movements and achieve the state-of-the-art accuracy by comparing against alternative methods and commercial system like VICON. Electronic supplementary materialThe online version of this article

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Section: Point Correspondencementioning

confidence: 99%

Toward accurate real-time marker labeling for live optical motion capture

Xia

Fei

et al. 2017

Vis Comput

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“…Hence, pose invariance is achieved by embedding the shape into an isometric subspace [21,12,4,29,25,15,16]. This is a very strong assumption for multicamera acquisition systems, as the independently reconstructed shapes can be non-isometric due to presence of topological merges and splits.…”

Section: Related Workmentioning

confidence: 99%

Topologically-robust 3D shape matching based on diffusion geometry and seed growing

et al. 2011

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Abstract3D Shape matching is an important problem in computer vision. One of the major difficulties in finding dense correspondences between 3D shapes is related to the topological discrepancies that often arise due to complex kinematic motions. In this paper we propose a shape matching method that is robust to such changes in topology. The algorithm starts from a sparse set of seed matches and outputs dense matching. We propose to use a shape descriptor based on properties of the heat-kernel and which provides an intrinsic scale-space representation. This descriptor incorporates (i) heat-flow from already matched points and (ii) self diffusion. At small scales the descriptor behaves locally and hence it is robust to global changes in topology. Therefore, it can be used to build a vertex-to-vertex matching score conditioned by an initial correspondence set. This score is then used to iteratively add new correspondences based on a novel seed-growing method that iteratively propagates the seed correspondences to nearby vertices. The matching is farther densified via an EM-like method that explores the congruency between the two shape embeddings. Our method is compared with two recently proposed algorithms and we show that we can deal with substantial topological differences between the two shapes.

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“…They showed that this method is more efficient than spin images in terms of recognition rate and efficiency. Sahillioglu and Yemez (2010) proposed an automatic technique to find correspondences between isometric shapes. They divided the data source and target into surface patches of equal area with each patch represented by the point at its center.…”

Section: Merging Cloud Of Pointsmentioning

confidence: 99%