Local shape descriptor selection for object recognition in range data

Taati, Babak; Greenspan, Michael

doi:10.1016/j.cviu.2010.11.021

Cited by 96 publications

(78 citation statements)

References 42 publications

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“…Other descriptors include 3D Tensor [1], Variable-Dimensional Local Shape Descriptors (VD-LSD) [35], 2.5D SIFT descriptor [36], SI-SIFT descriptor [37], Exponential Map (EM) [38], and Integral Invariants [39,40]. However, 3D Tensor is defined at the center of two points rather than any point of the input mesh, it is difficult to generate 3D Tensor descriptors at a set of given keypoints.…”

Section: Other Methodsmentioning

confidence: 99%

Performance Evaluation of 3D Local Feature Descriptors

Guo

Bennamoun

Sohel

et al. 2015

Computer Vision -- ACCV 2014

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Abstract. A number of 3D local feature descriptors have been proposed in literature. It is however, unclear which descriptors are more appropriate for a particular application. This paper compares nine popular local descriptors in the context of 3D shape retrieval, 3D object recognition, and 3D modeling. We first evaluate these descriptors on six popular datasets in terms of descriptiveness. We then test their robustness with respect to support radius, Gaussian noise, shot noise, varying mesh resolution, image boundary, and keypoint localization errors. Our extensive tests show that Tri-Spin-Images (TriSI) has the best overall performance across all datasets. Unique Shape Context (USC), Rotational Projection Statistics (RoPS), 3D Shape Context (3DSC), and Signature of Histograms of OrienTations (SHOT) also achieved overall acceptable results.

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Section: Other Methodsmentioning

confidence: 99%

Performance Evaluation of 3D Local Feature Descriptors

Guo

Bennamoun

Sohel

et al. 2015

Computer Vision -- ACCV 2014

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“…These feature correspondences are then used to generate candidate models and transformation hypotheses, which are nally veried to obtain the identity and pose of the object in the scene. In the process of object recognition, the descriptiveness and robustness of the local surface features play a signicant role [40]. A feature should, therefore, be descriptive and robust to a set of nuisances, including noise, varying mesh resolutions, occlusion, and clutter.…”

Section: Introductionmentioning

confidence: 99%

“…We also develop a hierarchical 3D object recognition algorithm. The performance of the object recognition algorithm was evaluated on four standard datasets (i.e., BoD1 [42], U3OR [30], the Queen's LIDAR Dataset (QuLD) [40], and the Ca' Fascari Venezia Dataset (CFVD) [35]). These datasets contain several nuisances including various object poses, dierent imaging techniques, noise, varying mesh resolutions, occlusion, and clutter.…”

Section: Introductionmentioning

confidence: 99%

“…Some examples are the Point Signature [4], spin image [23], 3D Shape Context (3DSC) [10], tensor [31], Variable-Dimensional Local Shape Descriptors (VD-LSD) [40], Exponential Map (EM) [1], Signature of Histograms of OrienTations (SHOT) [42], and Rotational Projection Statistics (RoPS) [17]. However, most of the existing local surface features suer from either non-uniqueness, low descriptiveness, weak robustness to noise, or high sensitivity to varying mesh resolutions [1] (see more details in Section 2).…”

Section: Introductionmentioning

confidence: 99%

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A novel local surface feature for 3D object recognition under clutter and occlusion

Guo

Sohel

Bennamoun

et al. 2015

Information Sciences

112

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“…[3][4][5] Given a database of 3-D models and a range image, the aim of object recognition is to identify the set of visible models and find the 3-D rigid transformations (i.e., rotations and translations) that can transform the visible models into the scene to superimpose the relative areas well. 6 However, to correctly recognize all the visible models in scenes with different levels of noise, varying mesh resolution, occlusion, and clutter is still a challenging work.…”

Section: Introductionmentioning

confidence: 99%

Simple and efficient improvement of spin image for three-dimensional object recognition

Hao

2016

Opt. Eng

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Abstract. This paper presents a highly distinctive and robust local three-dimensional (3-D) feature descriptor named longitude and latitude spin image (LLSI). The whole procedure has two modules: local reference frame (LRF) definition and LLSI feature description. We employ the same technique as Tombari to define the LRF. The LLSI feature descriptor is obtained by stitching the longitude and latitude (LL) image to the original spin image vertically, where the LL image was generated similarly with the spin image by mapping a two-tuple ðθ; ϕÞ into a discrete two-dimensional histogram. The performance of the proposed LLSI descriptor was rigorously tested on a number of popular and publicly available datasets. The results showed that our method is more robust with respect to noise and varying mesh resolution than existing techniques. Finally, we tested our LLSI-based algorithm for 3-D object recognition on two popular datasets. Our LLSI-based algorithm achieved recognition rates of 100%, 98.2%, and 96.2%, respectively, when tested on the Bologna, University of Western Australia (UWA) (up to 84% occlusion), UWA datasets (all). Moreover, our LLSI-based algorithm achieved 100% recognition rate on the whole UWA dataset when generating the LLSI descriptor with the LRF proposed by Guo.

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Local shape descriptor selection for object recognition in range data

Cited by 96 publications

References 42 publications

Performance Evaluation of 3D Local Feature Descriptors

Performance Evaluation of 3D Local Feature Descriptors

A novel local surface feature for 3D object recognition under clutter and occlusion

Simple and efficient improvement of spin image for three-dimensional object recognition

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