SHOT: Unique signatures of histograms for surface and texture description

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

doi:10.1016/j.cviu.2014.04.011

Cited by 713 publications

(499 citation statements)

References 42 publications

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“…SHOT 3,5 achieved a very low recall and precision with a high level of noise (with a deviation of 0.5 and 0.7 mr noise). It indicates that our LLSI descriptor is more robust than SHOT 3,5 to high level of noise.…”

Section: Robustness To Noisementioning

confidence: 90%

“…We compared our LLSI feature descriptor with two state-of-the-art feature descriptors, including spin image 11,12 and SHOT. 3,5 For this dataset, 1000 feature points were extracted from each model. As for the scenes, we extracted n × 1000 features per scene (n being the number of models present in the scene).…”

Section: Dataset and Parameter Settingmentioning

confidence: 99%

“…3,5 According to the former, a set of features encodes the geometric properties of the whole model while the latter relies on local key points and regional feature descriptions to determine point-to-point correspondence between surfaces. The global feature-based algorithms require complete 3-D models and are, therefore, sensitive to occlusion and clutter.…”

Section: Introductionmentioning

confidence: 99%

“…1,2 This technique has been used in numerous applications including automation, manipulation and grasping, robot localization and navigation, surgery and education. [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%

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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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Section: Robustness To Noisementioning

confidence: 90%

Section: Dataset and Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hao

2016

Opt. Eng

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“…Considering noise and outliers always existing in point clouds, the estimation of eigenvalues and eigenvectors will be susceptible to such disturbances. We adopt the weighted covariance matrix proposed in (Salti et al, 2014), assigning smaller weights to distant points in the covariance matrix of coordinates.…”

Section: Voxel Saliency Estimationmentioning

confidence: 99%

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Hoegner

Tuttas

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

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Feature Selection for 2Dand 3DFace Recognition

Bennamoun

Guo

Sohel

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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Face recognition is a popular research topic with a number of applications in several industrial sectors including security, surveillance, entertainment, virtual reality, and human–machine interaction. Both 2D images and 3D data can now be easily acquired and used for face recognition. For any 2D/3D face recognition system, feature extraction and selection play a significant role. Currently, both holistic and local features have been intensively investigated in the literature. In this article, fundamental background knowledge of face recognition, including 2D/3D data acquisition, data preprocessing, feature extraction, classification, and performance evaluation, is presented. The state‐of‐the‐art feature extraction algorithms, including 2D holistic feature, 2D local feature, 3D holistic feature, and 3D local feature extraction algorithms, are then described in detail. Finally, feature selection and fusion techniques are presented. The article covers the complete related aspects of feature selection for 2D and 3D face recognition.

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SHOT: Unique signatures of histograms for surface and texture description

Cited by 713 publications

References 42 publications

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

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

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Feature Selection for 2Dand 3DFace Recognition

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