Shape-similarity search of 3D models by using enhanced shape functions

Ohbuchi, Ryutarou; Minamitani, Takahiro; Takei, Tsuyoshi

doi:10.1109/tpcg.2003.1206936

Cited by 63 publications

(36 citation statements)

References 16 publications

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“…Some shape histogram methods accumulate the surface points in the bins [22], while density-based employ richer sets of multivariate shape features with a kernel strategy to estimate the distribution [20,55]. Ohbuchi et al [56] investigate shape histograms that are discretely parametrized along the principal axes of inertia of the model and also extended D2 shape function by considering the angle between the surfaces on which two random points are located [57]. This extension called as Absolute Angle-Distance histogram (AAD) outperformed the D2 shape function but at the cost of computation time.…”

Section: Related Workmentioning

confidence: 99%

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3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Ghorpade

Checchin

Laurent

et al. 2017

EURASIP J. Adv. Signal Process.

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The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

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Section: Related Workmentioning

confidence: 99%

“…The presented approach is completely different from Osada's except the use of PDFs to represent shape. Princeton Shape Benchmark [20,51,54,66] McGill 3D Shape Benchmark [61,63] CAD & VRML models [52,[56][57][58][59] Internet WWW (Polygonal meshes) [21,62] MPEG7 [61] ISDB, CDB, Sculpteur (SCU), SHREC Watertight (SHREC-W) [20,66] …”

Section: Introductionmentioning

confidence: 99%

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Ghorpade

Checchin

Laurent

et al. 2017

EURASIP J. Adv. Signal Process.

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show abstract

“…Global features computed to represent 3D objects include area, volume, and moments [13]. Some global shape distribution features computed include the angle between three random points (A3), the distance between a point and a random point (D1), the distance between two random points (D2), the area of the triangle between three random points (D3), and the volume between four random points on the surface (D4) [26,28]. Spatial map representations describe the 3D object by capturing and preserving physical locations on them [19-21,31].…”

Section: Related Literaturementioning

confidence: 99%

3D object retrieval using salient views

Atmosukarto

Shapiro

2012

Int J Multimed Info Retr

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This paper presents a method for selecting salient 2D views to describe 3D objects for the purpose of retrieval. The views are obtained by first identifying salient points via a learning approach that uses shape characteristics of the 3D points (Atmosukarto and Shapiro in International workshop on structural, syntactic, and statistical pattern recognition, 2008; Atmosukarto and Shapiro in ACM multimedia information retrieval, 2008). The salient views are selected by choosing views with multiple salient points on the silhouette of the object. Silhouette-based similarity measures from Chen et al. (Comput Graph Forum 22(3):223–232, 2003) are then used to calculate the similarity between two 3D objects. Retrieval experiments were performed on three datasets: the Heads dataset, the SHREC2008 dataset, and the Princeton dataset. Experimental results show that the retrieval results using the salient views are comparable to the existing light field descriptor method (Chen et al. in Comput Graph Forum 22(3):223–232, 2003), and our method achieves a 15-fold speedup in the feature extraction computation time.

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“…Ohbuchi et. al [12] improved the shape distribution method using a 2D histogram method which used the shape orientation distribution. Since the orientation information of a part is important for robot program generation, the 2D histogram method is adopted in this paper to find the best matching part.…”

Section: Transformative Industrial Robot Programming In Surface Manufmentioning

confidence: 99%

“…However, Osada's method does not compare the orientation distributions between two CAD models. To deal with the issue, Ohbuchi et al [12] developed a method using both the distance and orientation distributions of a part. Each point is assigned a normal based on the location of the point on the surface.…”

Section: B Cad Model Matchingmentioning

confidence: 99%

Transformative industrial robot programming in surface manufacturing

Chen

Sheng

2011

2011 IEEE International Conference on Robotics and Automation

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Surface manufacturing is a process of adding material to or removing material from the surfaces of a part, where industrial robots are usually used. Robot programming for these applications is still time consuming and costly. Typical teaching methods are not cost effective and efficient. There are many offline programming methods developed to reduce the robot programming effort. However, these methods suffer many practical issues, such as cable/hose tangling, robot configuration, collision and reachability. To solve these problems, this paper discusses a new method to generate robot programs based on existing ones in the database, which contains not only the robot paths, but also the programmers' knowledge and process parameters. Since industrial robots have been used in production for decades, there are many robot programs for different parts generated by the robot programmers. Some of these parts are quite similar, such as car bodies. Therefore, these robot programs can be transformed to generate new ones with some adjustments based on the geometry of new parts. In this paper, a robot path generation method is developed based on the existing robot paths in the database. Experiments were performed to validate the developed methodology. The results are very promising in reducing the programming efforts in surface manufacturing.

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Shape-similarity search of 3D models by using enhanced shape functions

Abstract: In this paper, we propose a pair of shape features for shape-similarity search of 3D polygonal-mesh models. The shape features are extension of the D2 shape functions proposed by Osada et al. [Osada01,Osada02]

Cited by 63 publications

References 16 publications

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

3D object retrieval using salient views

Transformative industrial robot programming in surface manufacturing

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