An overview of partial 3D object retrieval methodologies

Savelonas, Michalis A.; Pratikakis, Ioannis; Sfikas, Konstantinos

doi:10.1007/s11042-014-2267-9

Cited by 38 publications

(15 citation statements)

References 60 publications

(87 reference statements)

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“…Below, we focus on research areas related to the efforts in this paper. For a broad introduction to model recognition method, please refer to any of References [18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Research on Similarity Measurements of 3D Models Based on Skeleton Trees

et al. 2017

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Abstract:There is a growing need to be able to accurately and efficiently recognize similar models from existing model sets, in particular, for 3D models. This paper proposes a method of similarity measurement of 3D models, in which the similarity between 3D models is easily, accurately and automatically calculated by means of skeleton trees constructed by a simple rule. The skeleton operates well as a key descriptor of a 3D model. Specifically, a skeleton tree represents node features (including connection and orientation) that can reflect the topology and branch features (including region and bending degree) of 3D models geometrically. Node feature distance is first computed by the dot product between node connection distance, which is defined by 2-norm, and node orientation distance, which is defined by tangent space distance. Then branch feature distances are computed by the weighted sum of the average regional distances, as defined by generalized Hausdorff distance, and the average bending degree distance as defined by curvature. Overall similarity is expressed as the weighted sum of topology and geometry similarity. The similarity calculation is efficient and accurate because it is not necessary to perform other operations such as rotation or translation and it considers more topological and geometric information. The experiment demonstrates the feasibility and accuracy of the proposed method.

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“…Below, we focus on research areas related to the efforts in this paper. For a broad introduction to model recognition method, please refer to any of References [18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Research on Similarity Measurements of 3D Models Based on Skeleton Trees

et al. 2017

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“…Partial matching is a variant of the shape matching problem [LBZ*13, SPS14], according to which similarity assessment is restricted to shape parts, still in terms of correspondence [SHCB11, vKZH13] or numerical score [SSS*10, DLL*10, TDVC11, WZL*10]. Partial matching and the strictly related complementary matching problem are key issues, for example, for the reassembly and geometric auto‐completion of fragmented CH objects [GSP*14] and protein docking [SAHBZ08, ADPH11].…”

Section: Applications and Benchmarksmentioning

confidence: 99%

Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment

Biasotti

Cerri

Bronstein

et al. 2015

Computer Graphics Forum

123

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The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map‐based methods also define (dense) shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed.

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“…In the case of 3D video this problem is still open for research. Finally, it is worth to point out that work done on 3D object recognition techniques which can also be used in retrieval, as published in [73][74][75].…”

Section: Video Retrievalmentioning

confidence: 99%

Towards key-frame extraction methods for 3D video: a review

Ferreira

Cruz

Assunção

2016

J Image Video Proc.

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The increasing rate of creation and use of 3D video content leads to a pressing need for methods capable of lowering the cost of 3D video searching, browsing and indexing operations, with improved content selection performance. Video summarisation methods specifically tailored for 3D video content fulfil these requirements. This paper presents a review of the state-of-the-art of a crucial component of 3D video summarisation algorithms: the key-frame extraction methods. The methods reviewed cover 3D video key-frame extraction as well as shot boundary detection methods specific for use in 3D video. The performance metrics used to evaluate the key-frame extraction methods and the summaries derived from those key-frames are presented and discussed. The applications of these methods are also presented and discussed, followed by an exposition about current research challenges on 3D video summarisation methods.Keywords: 3D key-frames extraction, 3D video summarisation, Shot boundary detection ReviewIn the last years, new features have been implemented in video applications and terminal equipments due to users demand, who are always seeking for new viewing experiences more interactive and immersive, such as those provided by 3D video. This new visual experience is created by depth information that is part of 3D video and absent in classic 2D video. The inclusion of depth information in video signals is not a recent innovation, but the interest in this type of content and aspects related to it, such as acquisition, analysis, coding, transmission and visualisation, have been increasing recently [1,2]. Lately, 3D video has been attracting attention from industry, namely content producers, equipment providers, distributors and from the research community mostly on account of the improvements in Quality of Experience that it provides to viewers [3], as well as due to the new business opportunities presented by this emerging multimedia format. In the past, video repositories were relatively small so that indexing and retrieval operations were easy to perform. More recently, the massification of 3D video and its applications have resulted in the generation of huge amounts of data, increasing the need for methods that can efficiently index, search, browse and summarise the relevant information with minimum human intervention. Furthermore, 3D video description and management is also required to enable quick presentation of the most important information in a user-friendly manner [4,5]. Video summarisation is a video-content representation method that can fulfil these requirements. In contrast to summarisation of 2D video, which has been the subject of a significant amount of research, 3D video summarisation is still a relatively unexplored research problem which deserves more attention.A video summary is a short version of a full-length video that preserves the essential visual and semantic information of the original unabridged content. In the video summarisation process, a subset of key-frames or a set of shorter video...

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An overview of partial 3D object retrieval methodologies

Cited by 38 publications

References 60 publications

Research on Similarity Measurements of 3D Models Based on Skeleton Trees

Research on Similarity Measurements of 3D Models Based on Skeleton Trees

Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment

Towards key-frame extraction methods for 3D video: a review

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