An efficient registration algorithm based on spin image for LiDAR 3D point cloud models

He, Yuqing; Mei, Yuangang

doi:10.1016/j.neucom.2014.09.029

Cited by 16 publications

(10 citation statements)

References 27 publications

(27 reference statements)

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“…The compared methods has never been tested on our experimental datasets before. (2) In terms of range image registration, the prior evaluation work in [31] also shows that different descriptors behave quite different on across-dataset experiments. For examples, RoPS and SHOT achieve top-ranked performance in terms of object recognition, while they just behave moderately in 2.5D view matching case.…”

Section: Mash Vs the State-of-the-artsmentioning

confidence: 99%

“…The development of numerous low-cost 3D sensors, e.g., Microsoft Kinect and Intel RealSense, has further promoted such trend in recent years. Pairwise registration of range images is a fundamental and crucial step in many 3D vision tasks, such as 3D object recognition [1] , surface registration [2] , 3D modeling [3] , cultural heritage [4] and robotics [5] .…”

Section: Introductionmentioning

confidence: 99%

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Multi-attribute statistics histograms for accurate and robust pairwise registration of range images

2017

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Section: Mash Vs the State-of-the-artsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-attribute statistics histograms for accurate and robust pairwise registration of range images

2017

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“…The approach is investigated using a case study of spin-image algorithm (Johnson, 1997). This algorithm is selected because it is being used in a wide range of important applications including 3D Database Retrieval Sys-tems (Assfalg et al, 2004), Face Detection (Choi and Kim, 2013), Object Recognition (Johnson and Hebert, 1999), Object Categorization (Eleliemy et al, 2013), 3D map registration (Mei and He, 2013), and registration algorithm for LiDAR 3D point cloud models (He and Mei, 2015). The spin-image algorithm is well-known for its high computational complexity and is considered an essential bottleneck in many fields.…”

Section: Introductionmentioning

confidence: 99%

Loadbalancing on Parallel Heterogeneous Architectures: Spin-image Algorithm on CPU and MIC

Eleliemy

Fayze

Mehmood

et al. 2018

Linköping Electronic Conference Proceedings

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Loadbalancing of computational tasks over heterogeneous architectures is an area of paramount importance due to the growing heterogeneity of HPC platforms and the higher performance and energy efficiency they could offer. This paper aims to address this challenge for a heterogeneous platform comprising Intel Xeon multi-core processors and Intel Xeon Phi accelerators (MIC) using an empirical approach. The proposed approach is investigated through a case study of the spin-image algorithm, selected due to its computationally intensive nature and a wide range of applications including 3D database retrieval systems and object recognition. The contributions of this paper are threefold. Firstly, we introduce a parallel spin-image algorithm (PSIA) that achieves a speedup of 19.8 on 24 CPU cores. Secondly, we provide results for a hybrid implementation of PSIA for a heterogeneous platform comprising CPU and MIC: to the best of our knowledge, this is the first such heterogeneous implementation of the spin-image algorithm. Thirdly, we use a range of 3D objects to empirically find a strategy to loadbalance computations between the MIC and CPU cores, achieving speedups of up to 32.4 over the sequential version. The LIRIS 3D mesh watermarking dataset is used to investigate performance analysis and optimization.

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“…SHEN [7] create a lower dimension descriptor named LDFH to registrate clouds. While He Y propose a spin image based three-dimensional feature to registrate LiDAR point cloud models in [8]. And the concepts of balance factor is proposed in [9] to improve ICP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Rigid 3D Point Cloud Registration Based on Point Feature Histograms

Wang¹,

Zhang²

2017

Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

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Depending on the displacement and orientation between point clouds, the registration of scattered point clouds is offten divided into two steps: crude and fine alignment. An approach of point cloud classification based on point feature histogram was proposed in this paper. We propose a method of establishing the point feature histograms to match feature points in different clouds. To reject the outliers, Random Sample Consensus algorithm is used. The rigid transformation matrix in crude alignment is then computed by Singular Value Decomposition method. The golden standard for fine alignment is the Iterative Closest Point algorithm and its variants. In this paper we apply a dynamic constraint of distance to improve the traditional algorithm. The experiment shows that our process of registration works fine with higher accuracy and efficiency.

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An efficient registration algorithm based on spin image for LiDAR 3D point cloud models

Cited by 16 publications

References 27 publications

Multi-attribute statistics histograms for accurate and robust pairwise registration of range images

Multi-attribute statistics histograms for accurate and robust pairwise registration of range images

Loadbalancing on Parallel Heterogeneous Architectures: Spin-image Algorithm on CPU and MIC

Rigid 3D Point Cloud Registration Based on Point Feature Histograms

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