3D model retrieval using weighted bipartite graph matching

Gao, Yue; Dai, Qionghai; Wang, Meng; Zhang, Naiyao

doi:10.1016/j.image.2010.10.006

Cited by 103 publications

(46 citation statements)

References 34 publications

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“…2017, 9, 92 6 of 16 Subsequently, a bipartite graph G = {B, U, E} is constructed, where E represents the edges linking the vertices in point sets B and U. The bipartite graph G can be defined with |G| = |B| + |U| vertices, where |.| is the cardinality and |B| = |U| = n. According to [6,60,61], the weight of the edges from vertices in B to vertices in U is defined by the distance in the spatial domain, as shown in Figure 2b.…”

Section: Bipartite Graph Matchingmentioning

confidence: 99%

“…An increasing number of applications, such as urban planning, training simulations, virtual tourism, real-time emergency response, personal navigation, and homeland security, require 3D building models as an input source [2][3][4][5][6]. The 3D building models provide quick access to the urban topography, human-made structures, and our surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

et al. 2017

Remote Sensing

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3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds.

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Section: Bipartite Graph Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

et al. 2017

Remote Sensing

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“…With user feedback, these low level features are mapped to high level semantic space, which is another Euclidean space and can be regarded as a dimension reduction or feature selection method. A bipartite graph learning method is introduced in [38], where the comparison between two groups of multiple views is formulated in a bipartite graph. A learning-based method for bipartite graph matching is proposed in [39].…”

Section: Related Workmentioning

confidence: 99%

Learning View-Model Joint Relevance for 3D Object Retrieval

Lü

Xue

et al. 2015

IEEE Trans. on Image Process.

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Abstract-3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In this paper, we propose to jointly learn the view-model relevance among 3D objects for retrieval, in which the 3D objects are formulated in different graph structures. With the view information, the multiple views of 3D objects are employed to formulate the 3D object relationship in an object hypergraph structure. With the model data, the model-based features are extracted to construct an object graph to describe the relationship among the 3D objects. The learning on the two graphs is conducted to estimate the relevance among the 3D objects, in which the view/model graph weights can be also optimized in the learning process. This is the first work to jointly explore the view-based and model-based relevance among the 3D objects in a graphbased framework. The proposed method has been evaluated in three data sets. The experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness on retrieval accuracy of the proposed 3D object retrieval method.Index Terms-3D object retrieval, view information, model data, joint learning.

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“…We proposed to employ bipartite graphs to formulate the relationship between two groups of views from two 3-D objects [27]. The representative views are first generated from each object and all representative views from one object are denoted by one part of the bipartite graph.…”

Section: • Bipartite Graph Matching and Learningmentioning

confidence: 99%