Unique shape context for 3d data description

Tombari, Federico; Salti, Samuele; Stefano, Luigi Di

doi:10.1145/1877808.1877821

Cited by 308 publications

(198 citation statements)

References 19 publications

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“…From this sampling, we calculate five 3D descriptors for each object: ensemble of shape functions (ESF) [34], viewpoint feature histogram (VFH) [24], intrinsic spin images (SI) [32], signature of histograms of orientations (SHOT) [30] and unique shape contexts (USC) [4]). The reasons for choosing those descriptors are (1) uniqueness (preference to heterogeneity of algorithms) and (2) accessibility (the methods used are available from the point cloud library [25]).…”

Section: D Shape Descriptorsmentioning

confidence: 99%

Can Computer Vision Problems Benefit from Structured Hierarchical Classification?

Hoyoux

Rodríguez-Sánchez

Piater

et al. 2015

Computer Analysis of Images and Patterns

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Research in the field of supervised classification has mostly focused on the standard, so-called "flat" classification approach, where the problem classes live in a trivial, one-level semantic space. There is however an increasing interest in the hierarchical classification approach, where a performance gain is expected by incorporating prior taxonomic knowledge about the classes into the learning process. Intuitively, the hierarchical approach should be beneficial in general for the classification of visual content, as suggested by the fact that humans seem to organize objects into hierarchies based on visually perceived similarities. In this paper, we provide an analysis that aims to determine the conditions under which the hierarchical approach can consistently give better performances than the flat approach for the classification of visual content. In particular, we (1) show how hierarchical methods can fail to outperform flat methods when applied to real vision-based classification problems, and (2) investigate the underlying reasons for the lack of improvement, by applying the same methods to synthetic datasets in a simulation. Our conclusion is that the use of high-level hierarchical feature representations is crucial for

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Section: D Shape Descriptorsmentioning

confidence: 99%

Can Computer Vision Problems Benefit from Structured Hierarchical Classification?

Hoyoux

Rodríguez-Sánchez

Piater

et al. 2015

Computer Analysis of Images and Patterns

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“…Unique Shape Context (USC) [18]: This descriptor is analogous to the 3DSC but without the ambiguity in the azimuth direction, thanks to the definition of a local reference frame consisting of 3 unit vectors that replace the orientation of the North pole with the normal and are computed by a distance-weighted eigen-decomposition, followed by a sign disambiguation step [19].…”

Section: D Shape Contexts (3dsc)mentioning

confidence: 99%

Comparing 3D Descriptors for Local Search of Craniofacial Landmarks

Sukno

Waddington

Whelan

2012

Lecture Notes in Computer Science

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Abstract. This paper presents a comparison of local descriptors for a set of 26 craniofacial landmarks annotated on 144 scans acquired in the context of clinical research. We focus on the accuracy of the different descriptors on a per-landmark basis when constrained to a local search. For most descriptors, we find that the curves of expected error against the search radius have a plateau that can be used to characterize their performance, both in terms of accuracy and maximum usable range for the local search. Six histograms-based descriptors were evaluated: three describing distances and three describing orientations. No descriptor dominated over the rest and the best accuracy per landmark was strongly distributed among 3 of the 6 algorithms evaluated. Ordering the descriptors by average error (over all landmarks) did not coincide with the ordering by most frequently selected, indicating that a comparison of descriptors based on their global behavior might be misleading when targeting facial landmarks.

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“…On the other hand, USC was reported to perform slightly better than 3DSC in terms of precision-recall curves for a task of feature matching on synthetically transformed shapes (Tombari et al, 2010). However, USC was found considerably less accurate than 3DSC when targeting specific points on a craniofacial landmark localization task (Sukno et al, 2012).…”

Section: Introductionmentioning

confidence: 98%

“…Geometric descriptors for three dimensional (3D) data are important for a wide range of applications, as they constitute a core element for the identification of corresponding points in relation to object retrieval (Tombari et al, 2010), recognition (Frome et al, 2004), surface registration (Bariya et al, 2012) and landmark identification (Creusot et al, 2011;Passalis et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Rotationally Invariant 3D Shape Contexts using Asymmetry Patterns

Sukno

Waddington

Whelan

2013

Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Inform

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Abstract:This paper presents an approach to resolve the azimuth ambiguity of 3D Shape Contexts (3DSC) based on asymmetry patterns. We show that it is possible to provide rotational invariance to 3DSC at the expense of a marginal increase in computational load, outperforming previous algorithms dealing with the azimuth ambiguity. We build on a recently presented measure of approximate rotational symmetry in 2D defined as the overlapping area between a shape and rotated versions of itself to extract asymmetry patterns from a 3DSC in a variety of ways, depending on the spatial relationships that need to be highlighted or disabled. Thus, we define Asymmetry Patterns Shape Contexts (APSC) from a subset of the possible spatial relations present in the spherical grid of 3DSC; hence they can be thought of as a family of descriptors that depend on the subset that is selected. This provides great flexibility to derive different descriptors. We show that choosing the appropriate spatial patterns can considerably reduce the errors obtained with 3DSC when targeting specific types of points.

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Unique shape context for 3d data description

Cited by 308 publications

References 19 publications

Can Computer Vision Problems Benefit from Structured Hierarchical Classification?

Can Computer Vision Problems Benefit from Structured Hierarchical Classification?

Comparing 3D Descriptors for Local Search of Craniofacial Landmarks

Rotationally Invariant 3D Shape Contexts using Asymmetry Patterns

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