A Novel Local Surface Description for Automatic 3D Object Recognition in Low Resolution Cluttered Scenes

Shah, Syed Afaq Ali; Bennamoun, Mohammed; Boussaid, Farid; El-Sallam, Amar A.

doi:10.1109/iccvw.2013.88

Cited by 20 publications

(8 citation statements)

References 19 publications

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“…A wide variety of 3D keypoint detectors and feature descriptors have been proposed in the literature [11,9,12,8]. It is widely agreed that the evaluation of feature detectors and descriptors is very important [13].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Performance Evaluation of 3D Local Feature Descriptors

Guo

Bennamoun

Sohel

et al. 2015

Computer Vision -- ACCV 2014

Self Cite

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“…Major categories of the LRF are listed in Table 2. As is seen, one type [36,39,[57][58][59][60] is to calculate the three axes simultaneously by using eigenvectors, and so on and the other [61][62][63][64][65][66] to set up the axis one by one based on a normal line, and so on.…”

Section: Role and Classification Of Local Reference Framementioning

confidence: 99%

A Survey and Technology Trends of 3D Features for Object Recognition

Hashimoto

Akizuki

Takei

2017

Elect Comm in Japan

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SUMMARY Recently, various kinks of three‐dimensional (3D) imaging devices such as laser range finder or time‐of‐flight sensor become popular, so a lot of practical algorithms for recognizing 3D objects using “point cloud data” have been proposed. In this paper, we will introduce typical object recognition approaches and survey various kinds of 3D features proposed by many researchers. As for some important techniques, their principle and characteristics are explained in detail. Also we will mention about the local reference frame that is very important factor to realize stable feature description and accurate pose estimation in practical use.

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“…Engel et al [44] calculated the flux flow on the GVF and adopted it for pedestrian detection. Based on the 3D vector field, a rotation invariant descriptor called 3D-Div [45] was proposed for 3D object recognition by computing the divergence of the vector field. Nonetheless, the point-wise divergence in [45] cannot capture the neighborhood information of each point.…”

Section: Related Workmentioning

confidence: 99%

Structure-Preserving Binary Representations for RGB-D Action Recognition

Liu

Shao

2016

IEEE Trans. Pattern Anal. Mach. Intell.

101

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In this paper, we propose a novel binary local representation for RGB-D video data fusion with a structure-preserving projection. Our contribution consists of two aspects. Toacquire a general feature for the video data, we convert the problem to describing the gradient fields of RGB and depth information of video sequences. With the local fluxes of the gradient fields, which include the orientation and the magnitude of the neighborhood of each point, a new kind of continuous local descriptor called Local Flux Feature(LFF) is obtained. Then the LFFs from RGB and depth channels are fused into a Hamming space via the Structure Preserving Projection (SPP). Specifically, an orthogonal projection matrix is applied to preserve the pairwise structure with a shape constraint to avoid the collapse of data structure in the projected space. Furthermore, a bipartite graph structure of data is taken into consideration, which is regarded as a higher level connection between samples and classes than the pairwise structure of local features. Theextensive experiments show not only the high efficiency of binary codes and the effectiveness of combining LFFs from RGB-D channels via SPP on various action recognition benchmarks of RGB-D data, but also the potential power of LFF for general action recognition.

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A Novel Local Surface Description for Automatic 3D Object Recognition in Low Resolution Cluttered Scenes

Abstract: Abstract

Cited by 20 publications

References 19 publications

Performance Evaluation of 3D Local Feature Descriptors

Performance Evaluation of 3D Local Feature Descriptors

A Survey and Technology Trends of 3D Features for Object Recognition

Structure-Preserving Binary Representations for RGB-D Action Recognition

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