Less is More: Efficient 3-D Object Retrieval With Query View Selection

Gao, Yue; Wang, Meng; Zha, Zheng-Jun; Tian, Qi; Dai, Qionghai; Zhang, Naiyao

doi:10.1109/tmm.2011.2160619

Cited by 190 publications

(60 citation statements)

References 34 publications

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“…The digital information on the web has increased over the web and searching for particular information on a web is an important task .To search multimedia content such as image or video which is classified into two categories such as text based search and content based search [8] [9]. The approach of text based search uses textual queries, specification of term based desired media entities, for searching media data to match with them surrounding textual descriptor [10].…”

Section: Related Workmentioning

confidence: 99%

Multimedia Based Community Question Answer by Harvesting Web Based Information

Ambatkar¹,

Pujari²

2014

IOSRJCE

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Section: Related Workmentioning

confidence: 99%

Multimedia Based Community Question Answer by Harvesting Web Based Information

Ambatkar¹,

Pujari²

2014

IOSRJCE

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“…Finally, it is interesting to mention that other proposals focus on the problem of retrieving 3D object models (e.g, [31,32,33,34,35]) or videos (e.g., [36]) instead of images. To improve the efficiency and accuracy of view-based 3D object retrieval, in [35] the authors propose to select the most interesting 2D views using a probabilistic Bayesian method (Adaptive Views Clustering), whereas in [33] the authors present an algorithm that minimizes the number of query views required based on information extracted from the query and the users' relevance feedback.…”

Section: Related Workmentioning

confidence: 99%

Real-time selection of video streams for live TV broadcasting based on Query-by-Example using a 3D model

Yus

Ilarri

Mena

2013

Multimed Tools Appl

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The emergence of low-cost cameras with nearly professional features in the consumer market represents a new important source of video information. For example, using an increasing number of these cameras in live TV broadcastings enables obtaining varied contents without affecting the production costs. However, searching for interesting shots (e.g., a certain view of a specific car in a race) among many video sources in real-time can be difficult for a Technical Director (TD). So, TDs require a mechanism to easily and precisely represent the kind of shot they want to obtain abstracting them from the need to be aware of all the views provided by the cameras.In this paper we present our proposal to help a TD to visually define, using an interface for the definition of 3D scenes, an interesting sample view of one or more objects in the scenario. We recreate the views of the cameras in a 3D engine and apply 3D geometric computations on their virtual view, instead of analyzing the real images they provide, to enable an efficient and precise realtime selection. Specifically, our system computes a similarity measure to rank the candidate cameras. Moreover, we present a prototype of the system and an experimental evaluation that shows the interest of our proposal.

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“…For view-based retrieval, 3D shapes are represented as a set of 2D projections and 3D models are matched using their visual similarities rather than the geometric similarities (Gao et al, 2011;Gao et al, 2012;Chen et al, 2003;Stavropoulos et al, 2010;Papadakisa et al, 2007). Each projection is described by image descriptors.…”

Section: Introductionmentioning

confidence: 99%

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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Less is More: Efficient 3-D Object Retrieval With Query View Selection

Cited by 190 publications

References 34 publications

Multimedia Based Community Question Answer by Harvesting Web Based Information

Multimedia Based Community Question Answer by Harvesting Web Based Information

Real-time selection of video streams for live TV broadcasting based on Query-by-Example using a 3D model

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

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