3D Model Retrieval Using Probability Density-Based Shape Descriptors

Akgül, Ceyhun Burak; Sankur, B.; Yemez, Y.; Schmitt, Francis

doi:10.1109/tpami.2009.25

Cited by 104 publications

(62 citation statements)

References 38 publications

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“…like.com/, logo and trademark retrieval, 27 handwritten digit recognition, 27 and retrieval of threedimensional computer models. 28 Single object-based shape description has also been successfully employed in several medical CAD applications such as in CT colonography 29,30 and lung nodule detection. 31 A shape-based representation of the image content in the form of point sets, contours, curves, regions, or surfaces should be available for the computation of shape-based features.…”

Section: Image Features/descriptorsmentioning

confidence: 99%

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

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Section: Image Features/descriptorsmentioning

confidence: 99%

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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“…Most previous studies focus on encoding and retrieving 3D polygon models using polygon models as input queries (Funkhouser et al, 2003;Assfalg et al, 2007;Gao et al, 2011;Akgul et al, 2009;Gao et al, 2012). However, these studies do not consider model retrieval by using point clouds, which is in a great need in the topic of efficient cyber city construction with airborne LiDAR point clouds.…”

Section: Introductionmentioning

confidence: 99%

“…Following the categorization in (Akgul et al, 2009), 3D model retrieval methods are classified into two categories, model-based retrieval and view-based 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).…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…These conditions enable a topology-based method to facilitate efficient shape matching. In the methods of shape distribution (Assfalg et al, 2007;Akgul et al, 2009), geometric features are accumulated in bins that are defined over feature spaces. A histogram of these values is then used as the signature of a 3D model.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Lin

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Theme Sessions, ThS2KEY WORDS: Point Cloud Encoding, Spatial Histogram, 3D Model Retrieval, Cyber City Modeling ABSTRACT:With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

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Three Dimensional Shape Descriptor

Tombari

2014

Computer Vision

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3D Model Retrieval Using Probability Density-Based Shape Descriptors

Cited by 104 publications

References 38 publications

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

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

Three Dimensional Shape Descriptor

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