Sketch-based 3D model retrieval using diffusion tensor fields of suggestive contours

Yoon, Sang Min; Scherer, Maximilian; Schreck, Tobias; Kuijper, Arjan

doi:10.1145/1873951.1873961

Cited by 88 publications

(74 citation statements)

References 27 publications

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“…So far, sketch retrieval had formed 8 benchmark, they were as following: Snograss and Vanderwart [12] proposed the standard line drawings(1980), Cole et al [13]'s line drawing benchmark (2008), Saavedra and Bustos [14]'s sketch dataset (2010), Yoon et al [15]'s sketch-based 3D model retrieval benchmark (2010), Eitz et al [9]'s sketch-based shape retrieval benchmark, Eitz et al's [10] sketch recognition benchmark(2012), Small-scale benchmark [11]: Shrec'12 Sketch Track Benchmark, large-scale: Shrec'13 Sketch Track Benchmark [16].These benchmarks played an important role in the research and application of sketch retrieval.…”

Section: The Related Researchmentioning

confidence: 99%

SVM: Sketch-based 3D Retrieval Application Using Classification Method

Zhou¹,

Jia²

2017

dtcse

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Abstract. With the rapid development of 3D technology, the demand to use and retrieve the 3D model is becoming more and more urgent. In this case, it is more and more import and necessary that sketch-based 3D model retrieval. The user provides a hand-drawn sketch, then the system can be provided the possible list of models, then the user can select the need model from this list. In this paper, it's proposed a framework of using SVM classification methods and K-means cluster algorithm. In preprocess part; the adaptive thinning algorithm is used to process the input sketch. In the offline part, the model is projected to multi-view images, and then SVM classifier has been used to classify these images. Moreover, we extract the images features, and use K-means algorithm to cluster these features. Finally, the feature dictionary will be acquired. In online process part, it only needs to extract the input sketch feature, and computes the distance between dictionary and their features. Besides, the experiment is realized to verify the feasibility of the approach. Finally, it was compared with other's approaches; the result shows that the approach is viable and robustness.

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Section: The Related Researchmentioning

confidence: 99%

SVM: Sketch-based 3D Retrieval Application Using Classification Method

Zhou¹,

Jia²

2017

dtcse

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“…There are many works on 3D model retrieval which allow users to sketch the contour of their desired object and thus guide a DB search [36], [37], [38], [39], [40]. In these works, the user is required to sketch the 2D shape of the object as seen from one or more viewpoints.…”

Section: Modeling Interfacementioning

confidence: 99%

Tree Modeling with Real Tree-Parts Examples

Xie

Yan

Sharf

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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Abstract-We introduce a 3D tree modeling technique that utilizes examples of real trees to enhance tree creation with realistic structures and fine-level details. In contrast to previous works that use smooth generalized cylinders to represent tree branches, our method generates realistic looking tree models with complex branching geometry by employing an exemplar database consisting of real-life trees reconstructed from scanned data. These trees are sliced into representative parts (denoted as tree-cuts), representing trunk logs and branching structures. In the modeling process, tree-cuts are positioned in space in an intuitive manner, serving as efficient proxies that guide the creation of the complete tree. Allometry rules are taken into account to ensure reasonable relations between adjacent branches. Realism is further enhanced by automatically transferring geometric textures from our database onto tree branches as well as by guided growing of foliage. Our results demonstrate the complexity and variety of trees that can be generated with our method within few minutes. We carry a user study to test the effectiveness of our modeling technique.

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“…For instance, Funkhouser et al used a variant of the 3D sphere harmonics to develop a shape search engine that accepts sketches as queries [2]. Yoon et al employed suggestive contours and diffusion tensor fields to improve the robustness against shape and pose variance that often occurs in the user sketched images [17]. More recently, Shao et al utilized a combination of contour-based representation and dense 2D matching to develop a robust approach that could perform partial matching between a query sketch and 3D models [18].…”

Section: Related Workmentioning

confidence: 99%

From Low-Cost Depth Sensors to CAD: Cross-Domain 3D Shape Retrieval via Regression Tree Fields

Wang

Feng

et al. 2014

Computer Vision – ECCV 2014

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Abstract. The recent advances of low-cost and mobile depth sensors dramatically extend the potential of 3D shape retrieval and analysis. While the traditional research of 3D retrieval mainly focused on searching by a rough 2D sketch or with a high-quality CAD model, we tackle a novel and challenging problem of cross-domain 3D shape retrieval, in which users can use 3D scans from low-cost depth sensors like Kinect as queries to search CAD models in the database. To cope with the imperfection of user-captured models such as model noise and occlusion, we propose a cross-domain shape retrieval framework, which minimizes the potential function of a Conditional Random Field to efficiently generate the retrieval scores. In particular, the potential function consists of two critical components: one unary potential term provides robust crossdomain partial matching and the other pairwise potential term embeds spatial structures to alleviate the instability from model noise. Both potential components are efficiently estimated using random forests with 3D local features, forming a Regression Tree Field framework. We conduct extensive experiments on two recently released user-captured 3D shape datasets and compare with several state-of-the-art approaches on the cross-domain shape retrieval task. The experimental results demonstrate that our proposed method outperforms the competing methods with a significant performance gain.

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Sketch-based 3D model retrieval using diffusion tensor fields of suggestive contours

Cited by 88 publications

References 27 publications

SVM: Sketch-based 3D Retrieval Application Using Classification Method

SVM: Sketch-based 3D Retrieval Application Using Classification Method

Tree Modeling with Real Tree-Parts Examples

From Low-Cost Depth Sensors to CAD: Cross-Domain 3D Shape Retrieval via Regression Tree Fields

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