A survey of content based 3D shape retrieval methods

Tangelder, Johan W. H.; Veltkamp, Remco C.

doi:10.1007/s11042-007-0181-0

Cited by 712 publications

(186 citation statements)

References 105 publications

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“…It includes problematics that arise from point cloud data management in terms of storing and efficient structuration for semantic and spatial demands. Indeed, existing PCDBMS (point cloud database management systems) and indexing techniques provide a solution to storing, compressing and managing the data (Dobos et al, 2014;Richter and Döllner, 2013;van Oosterom et al, 2015), but efficiency and extensibility to dynamic semantic update and ontological reasoning stays limited. Queries over octree derived indexing techniques can provide an efficient solution for out-of-core rendering and parallel processing, but data structuration cannot efficiently include context adaptation and inference reasoning.…”

Section: Transition To Point Cloudsmentioning

confidence: 99%

“…As stated by (Tangelder and Veltkamp, 2007) "any fully-fledged system should apply as much domain knowledge as possible, in order to make shape retrieval effective". With the rise of online solutions, we have seen a great potential in using knowledge database for classification to analogically associate shapes and groups of points with similar features.…”

Section: Level-2: Domain Adaptationmentioning

confidence: 99%

See 1 more Smart Citation

Model for Semantically Rich Point Cloud Data

Poux

Neuville

Hallot

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.

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Section: Transition To Point Cloudsmentioning

confidence: 99%

Section: Level-2: Domain Adaptationmentioning

confidence: 99%

Model for Semantically Rich Point Cloud Data

Poux

Neuville

Hallot

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

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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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“…The class shape_Des_3D has subclasses: Geometry_based, Feature_based, Graph_based as given by Tangelder et al in reference (Tangelder & Veltkamp, 2008). The Geometry_based has subclasses: Weighted_point_set, Deformation_based, Volumetric_error and View_based.…”

Section: Ontology Structurementioning

confidence: 99%

3D Virtual World Retrieval Based on Ontology and Content

Lmaati¹,

Ouahman²,

Kaddioui³

et al. 2014

CIS

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On the Web and on informatics systems, 3D virtual worlds used become big both in number and in size. Therefore, we propose in this paper a new method for retrieving 3D virtual worlds based on the semantics and the content. Firstly, we propose a new classified database of 3D virtual worlds given in VRML format. To achieve the semantic method, we construct an ontology that describes virtual worlds in various aspects including their contents (3D objects building a virtual world) and information about their contents (authors, file format, etc). This ontology is presented by OWL, the W3C recommended language. So as to extract desired 3D scenes from the proposed database we use the SPARQL query language. We propose finally a shape based method for searching desired virtual worlds by content in this database. This method is based on a new distance (metric) that define the similarity between virtual worlds. This method is evaluated using the recall vs. precision curves.

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A survey of content based 3D shape retrieval methods

Abstract: Abstract

Cited by 712 publications

References 105 publications

Model for Semantically Rich Point Cloud Data

Model for Semantically Rich Point Cloud Data

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

3D Virtual World Retrieval Based on Ontology and Content

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