A parts-based method for articulated target recognition in laser radar data

Computer Vision -- ACCV 2014

et al. 2015

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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.

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of 3D Local Feature Descriptors

Computer Vision -- ACCV 2014

et al. 2015

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“…Because of the rapid technological development of 3D imaging sensors, point clouds are becoming more available and accessible. The data availability together with the progress 2 in high-speed computing devices have contributed to the ourishing of research on 3D object recognition algorithms in recent years [19,16].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

Information Sciences

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111

“…This has numerous application areas including education (e.g., electronic museums and multimedia books), entertainment (e.g., 3D TV, games and movies), cultural heritage (e.g., reconstruction of historical relics), medical industry (e.g., orthodontics and diagnosis), manufacturing (e.g., prototyping and inspection) and robotics (e.g., navigation and object/face recognition) [1]- [5]. A model can be built using either Computer Aided Design (CAD) tools or 3D scanning equipments.…”

Section: Introductionmentioning

confidence: 99%

An Accurate and Robust Range Image Registration Algorithm for 3D Object Modeling

et al. 2014

IEEE Trans. Multimedia

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136

Abstract-Range image registration is a fundamental research topic for 3D object modeling and recognition. In this paper, we propose an accurate and robust algorithm for pairwise and multi-view range image registration. We first extract a set of Rotational Projection Statistics (RoPS) features from a pair of range images, and perform feature matching between them. The two range images are then registered using a transformation estimation method and a variant of the Iterative Closest Point (ICP) algorithm. Based on the pairwise registration algorithm, we propose a shape growing based multi-view registration algorithm. The seed shape is initialized with a selected range image and then sequentially updated by performing pairwise registration between itself and the input range images. All input range images are iteratively registered during the shape growing process. Extensive experiments were conducted to test the performance of our algorithm. The proposed pairwise registration algorithm is accurate, and robust to small overlaps, noise and varying mesh resolutions. The proposed multi-view registration algorithm is also very accurate. Rigorous comparisons with the state-of-theart show the superiority of our algorithm.