Sketch-based 3D model retrieval by incorporating 2D-3D alignment

In this paper, we address the evaluation of algorithms for partial shape retrieval using a large-scale simulated benchmark of partial views which are used as queries. Since the scanning of real objects is a time-consuming task, we create a simulation that generates a set of views from a target model and at different levels of complexity (amount of missing data). In total, our benchmark contains 7,200 partial views. Furthermore, we propose the use of weighted effectiveness measures based on the complexity of a query. With these characteristics, we aim at jointly evaluating the effectiveness, efficiency and robustness of existing algorithms. As a result of our evaluation, we found that a combination of methods provides the best effectiveness, mainly due to the

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“…-Range scan-based 3D model retrieval by incorporating 2D-3D alignment by Li et al [14,16]. This method is presented in Sect.…”

Section: Methodsmentioning

confidence: 99%

“…The retrieval algorithm is a modified version of the sketchbased 30 model retrieval algorithm proposed in [14]. The main steps are described in Fig.…”

Section: Range Scan-based 3d Model Retrieval By Incorporating 2d-3d Amentioning

confidence: 99%

A benchmark of simulated range images for partial shape retrieval

et al. 2014

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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%

“…In [32] the authors present an algorithm to retrieve 3D models by querying-by-sketch based on the alignment of 3D models to 2D sketches. In [31] an Interaction Metadata Query sub-Language is presented to enable 3D model retrieval using interaction metadata.…”

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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“…Many 3D models are now downloadable for free thanks to the internet, as well as the large databases of 3D models have become available in the market. The retrieval of these models in databases demands sophisticated methods for a rapid and effective response.Among the indexation approaches of 3D models, we find the 2D/3D approach (Haris et al, 2014;Jain and Singh, 2013;Li and Johan, 2013;Petre et al, 2010) which is based on a set of images (the characteristic views, images of depths, the slices...). The importance of this approach is extract for each image a descriptor vector for construct a set of vectors descriptors associated with the object to index.…”

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confidence: 99%

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Elkhal¹,

Lakehal²,

Satori³

2014

Journal of Computer Science

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In this study, we propose a new method for indexing and retrieval of 3D models in large databases based on binary images extracted from the 3D object called "level cut" LC. These cuts are obtained by the intersection of the set of the plans with the 3D object. A set of equidistant parallel plans generates by the intersection with the 3D object a set of cuts that used to indexing the 3D model. We are based on these cuts to describe the 3D object by using the vectors descriptors based on these cuts. To validate our descriptor we extract a test database from the NTU base. The robustness of our descriptor is well demonstrated by the comparison with the two will known descriptors, the 3D Zernike descriptor and the 3D invariant moment's descriptor. The topological problem of the external surfaces that representing the 3D object has been confronted, that shows the superiority of the cut method, because it is keeping the external surfaces details of the 3D object during the cutting step.Keywords: Characteristics Level Cut, Vector Descriptor, 3D Shape Indexing and Retrieval, X-means Algorithm, Similarity Measuring INTROCUCTIONRecently, the technological development of 3D modeling tools is forever exploited. Many 3D models are now downloadable for free thanks to the internet, as well as the large databases of 3D models have become available in the market. The retrieval of these models in databases demands sophisticated methods for a rapid and effective response.Among the indexation approaches of 3D models, we find the 2D/3D approach (Haris et al., 2014;Jain and Singh, 2013;Li and Johan, 2013;Petre et al., 2010) which is based on a set of images (the characteristic views, images of depths, the slices...). The importance of this approach is extract for each image a descriptor vector for construct a set of vectors descriptors associated with the object to index. The similarity measure between two 3D objects then returns to calculate the distance between the two sets of descriptors representing them. The extraction of information of the 3D object in a robust and efficient way Recently, the technological development of 3D has become a real challenge. The proposed method permits to deal with this problem. Given a 3D object, the method starts by a normalization step of the processed object using the CPCA technique. Then, we extract a set of cuts based on our cutting method. This set of cuts (binary images) will be indexed by a 2D image descriptor to construct the descriptor vector associated to the 3D model, using the Hausdorff distance to calculate the distance between the vectors descriptors which allows us to measure the similarity between the 3D objects.The proposed descriptor shows the robustness in terms of response to the query provided by the user and the comparative study between the proposed descriptor and two other descriptors well known of the 3D/3D approach, the 3D Zernike moments and 3D invariant moments shows considerable results.

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Sketch-based 3D model retrieval by incorporating 2D-3D alignment

Cited by 40 publications

References 30 publications

A benchmark of simulated range images for partial shape retrieval

A benchmark of simulated range images for partial shape retrieval

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

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

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