3D Model Retrieval with Spherical Harmonics and Moments

Saupe, Dietmar; Vranić, Dejan V.

doi:10.1007/3-540-45404-7_52

Cited by 143 publications

(100 citation statements)

References 7 publications

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“…Global approaches recognize objects by relying on global features, i.e., features extracted from the complete 3D geometry of the point cloud. Examples include spherical harmonics [18,35], shape moments [35], and shape histograms [29]. It is difficult to handle partial shapes using these approaches, since global features are sensitive to both absence of shape parts and occurrence of clutter.…”

Section: Global Approaches Vs Local Approachesmentioning

confidence: 99%

Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

Pham

Woodford

Perbet

et al. 2013

Machine Learning for Computer Vision

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Abstract. This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this space-the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach.

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Section: Global Approaches Vs Local Approachesmentioning

confidence: 99%

Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

Pham

Woodford

Perbet

et al. 2013

Machine Learning for Computer Vision

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“…Vranic et al [3] proposed the ray-based descriptor which characterizes a 3D model by a spherical extent function capturing the furthest intersection points of the model's surface with rays emanating from the origin. Spherical harmonics and moments representation of the spherical extent function were tested in [4], indicating better performance in the first case. In the same context, a more general approach was introduced by Vranic et al [5,6] known as the layered depth spheres descriptor (also known as radialized spherical extent function).…”

Section: Related Workmentioning

confidence: 99%

Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation

Papadakis

Pratikakis

Perantonis

et al. 2007

Pattern Recognition

167

105

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We present a 3D shape retrieval methodology based on the theory of spherical harmonics. Using properties of spherical harmonics, scaling and axial flipping invariance is achieved. Rotation normalization is performed by employing the continuous principal component analysis along with a novel approach which applies PCA on the face normals of the model. The 3D model is decomposed into a set of spherical functions which represents not only the intersections of the corresponding surface with rays emanating from the origin but also points in the direction of each ray which are closer to the origin than the furthest intersection point. The superior performance of the proposed methodology is demonstrated through a comparison against state-of-the-art approaches on standard databases. ᭧

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“…The FVs can be obtained by a variety of methods, from very simple ones (bounding box, area-volume ratio, eccentricity) to very complex ones (curvature distribution of sliced volume, spherical harmonics, 3D Fourier coefficients) [3][4][5]. The intrinsic nature of the objects may pose some constraints, and some methods may be more suitable, and faster, for the extraction of FVs than others.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Retrieval of 3D Polygonal Objects Based on Multiresolution Signatures

Lam

Buf

2011

Advances in Visual Computing

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Abstract. In this paper we present a method for retrieving 3D polygonal objects by using two sets of multiresolution signatures. Both sets are based on the progressive elimination of object's details by iterative processing of the 3D meshes. The first set, with five parameters, is based on mesh smoothing. This mainly affects an object's surface. The second set, with three parameters, is based on difference volumes after successive mesh erosions and dilations. Characteristic feature vectors are constructed by combining the features at three mesh resolutions of each object. In addition to being invariant to mesh resolution, the feature vectors are invariant to translation, rotation and size of the objects. The method was tested on a set of 40 complex objects with mesh resolutions different from those used in constructing the feature vectors. By using all eight features, the average ranking rate obtained was 1.075: 37 objects were ranked first and only 3 objects were ranked second. Additional tests were carried out to determine the significance of individual features and all combinations. The same ranking rate of 1.075 can be obtained by using some combinations of only three features.

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3D Model Retrieval with Spherical Harmonics and Moments

Cited by 143 publications

References 7 publications

Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation

Retrieval of 3D Polygonal Objects Based on Multiresolution Signatures

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