A geometric approach to 3D object comparison

Novotni, Marcin; Klein, Reinhard

doi:10.1109/sma.2001.923387

Cited by 75 publications

(50 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature vectors for 3D model retrieval can be based on Fourier descriptors of silhouettes [1,5], on 3D moments [8], rendered images or depth maps [5], or on volumetric representation of the model surface [2] or the corresponding volume (if the surface bounds a solid) [7,6,8].…”

Section: Previous Workmentioning

confidence: 99%

“…Many similarity-based retrieval systems were designed for still image, audio and video, while only a few techniques for content-based 3D model retrieval have been reported [2,[5][6][7][8][9][10][11]. We consider 3D object retrieval in which a 3D model given as a triangle mesh serves as a query key and similar objects are retrieved from a collection of 3D objects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3D Model Retrieval with Spherical Harmonics and Moments

Saupe

Vranić

2001

Lecture Notes in Computer Science

143

View full text Add to dashboard Cite

Abstract. We consider 3D object retrieval in which a polygonal mesh serves as a query and similar objects are retrieved from a collection of 3D objects. Algorithms proceed first by a normalization step in which models are transformed into canonical coordinates. Second, feature vectors are extracted and compared with those derived from normalized models in the search space. In the feature vector space nearest neighbors are computed and ranked. Retrieved objects are displayed for inspection, selection, and processing. Our feature vectors are based on rays cast from the center of mass of the object. For each ray the object extent in the ray direction yields a sample of a function on the sphere. We compared two kinds of representations of this function, namely spherical harmonics and moments. Our empirical comparison using precision-recall diagrams for retrieval results in a data base of 3D models showed that the method using spherical harmonics performed better.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Model Retrieval with Spherical Harmonics and Moments

Saupe

Vranić

2001

Lecture Notes in Computer Science

143

View full text Add to dashboard Cite

show abstract

“…Graph-based [29,30] 3. Geometry-based [31][32][33][34][35][36][37][38] Fig. 1 Taxonomical classification of shape analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

“…It is based on calculating a volumetric error between one object and a sequence of offset hulls of the other object [34]. Sánchez-Cruz and Bribiesca [33] presented a method which relates the volumetric error between two voxelized shapes to a transportation distance measuring how many voxels have to move, and how far, to change one shape into another.…”

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.

View full text Add to dashboard Cite

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.

show abstract

“…A cord is a vector from the center of mass of the model to the center of mass of a triangle. If the shape is solid, i.e., if an inside-outside test can be performed, the model can be converted to a voxel-based representation for feature extraction and distance computation [Novotni01]. Or, the shape may be given in a voxel representation to start with [Keim99].…”

Section: Introductionmentioning

confidence: 99%

Shape-similarity search of three-dimensional models using parameterized statistics

Ohbuchi

Otagiri

Ibato

et al.

10th Pacific Conference on Computer Graphics and Applications, 2002. Proceedings.

View full text Add to dashboard Cite

In this paper, we propose a method for shape-similarity search of 3D polygonal-mesh models. The system accepts triangular meshes, but tolerates degenerated polygons, disconnected component, and other anomalies. As the feature vector, the method uses a combination of three vectors, (1) the moment of inertia, (2) the average distance of surface from the axis, and (3) the variance of distance of the surface from the axis. Values in each vector are discretely parameterized along each of the three principal axes of inertia of the model. We employed the Euclidean distance and the elastic-matching distance as the measures of distance between pairs of feature vectors. Experiments showed that the proposed shape features and distance measures perform fairly well in retrieving models having similar shape from a database of VRML models.

show abstract

A geometric approach to 3D object comparison

Cited by 75 publications

References 13 publications

3D Model Retrieval with Spherical Harmonics and Moments

3D Model Retrieval with Spherical Harmonics and Moments

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

Shape-similarity search of three-dimensional models using parameterized statistics

Contact Info

Product

Resources

About