Registration of Color Point Cloud by Combining with Color Moments Information

Chen, Weile; Yang, Yang; Fan, Dandan; Chen, Zhuo; Kou, Qian

doi:10.1109/smc.2018.00362

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The algorithms for comparing include the traditional ICP algorithm [26], the ICP-4D method [24] which uses the Hue value in HSL color space to assist registration, the improved trimmed ICP algorithm (ITrICP) [13], our previous method ICP-CM in the conference version [9], which used color moment features based on ITrICP, and the state-of-the-art CPCRR color registration algorithm [28]. Same as the proposed method, these methods are all based on the point feature.…”

Section: Resultsmentioning

confidence: 99%

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Color Point Cloud Registration Based on Supervoxel Correspondence

et al. 2020

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With the development of RGBD sensors, the high-quality color point cloud can be obtained expediently. In this paper, we propose a novel registration method for 3D color point clouds from different views, which is a critical issue in many applications. Different from traditional feature-based methods, we design a hybrid feature representation with color moments of the point, which could be applied naturally for any color point cloud. And these features are extracted from point clouds based on the supervoxel segmentation. By jointly conducting these features for similarity measure, a weight parameter is dynamically adapted between the color and the spatial information. The registration algorithm is under a classic iterative framework for building the correspondence and estimating transformation parameters. In addition, we provide a mutual correspondence matching condition with hybrid features to build some more robust relationships for estimating transformation parameters. Experimental results demonstrate that our method can effectively reduce the number of point data for registration and achieve good matching results even in a poor initial condition. INDEX TERMSColor point cloud registration, hybrid feature, mutual correspondence matching.

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Section: Resultsmentioning

confidence: 99%

“…By filtering out the ambiguous feature relationships, the transformation estimation could be more accurate for robust registration. This paper is an extension version of [9]. Comparing with the previous work, more improvements are provided in the pre-processing for the large-scale data to improve the efficiency.…”

Section: Introductionmentioning

confidence: 99%

Color Point Cloud Registration Based on Supervoxel Correspondence

et al. 2020

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“…In 2018, Chen WeiLe et al 9 proposed a method for color point clouds registration based on color moment information. This method dynamically combines the structural and color features of each point to establish a correspondence between point clouds, and it demonstrates better robustness when dealing with geometric defects, missing data, and poor initial positions.…”

Section: Introductionmentioning

confidence: 99%

Coarse registration of color point clouds by combining color feature and geometric feature

Sun,

Jiang,

Luan

et al. 2024

MIPPR 2023: Pattern Recognition and Computer Vision

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Coarse registration is the initial step of aligning a point clouds with other clouds, aiming to put two point clouds in the correct position. There are many coarse registration methods, and among them, the SAC-IA (Sample Consensus Initial Alignment) is widely used. It selects corresponding point pairs by matching the geometric features of point clouds. It has high registration accuracy and fast registration speed. However, when the geometric features of the point clouds are relatively simple or not distinctive, its registration performance may not be very effective. With the development of color image sensors, acquiring color point clouds has become increasingly important. The RGB information of point clouds can effectively compensate for the shortcomings of the SAC-IA algorithm. The color characteristics of the interested points are extracted by fusing the color information of feature points and their neighboring points, including the first-order moment of point clouds color and the CFH of the point clouds, based on the traditional SAC-IA algorithm. Experiments have shown that the improved SAC-IA algorithm has better accuracy and robustness.

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“…The SIFT operator can reduce the influence of scale change on key point search, but its computation is complex [11,12]. In the paper [13], a registration method combining with color moment information improves the registration accuracy. In the literature [14], the future points are obtained via 3D Difference of Gaussians over geometric scalar values of the points which ensures obtaining salient features.…”

Section: Introductionmentioning

confidence: 99%

Automatic point cloud registration algorithm based on the feature histogram of local surface

Wang

Hua

et al. 2020

PLoS ONE

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In this paper, we present an efficient algorithm for point cloud registration in presence of low overlap rate and high noise. The proposed registration method mainly includes four parts: the loop voxel filtering, the curvature-based key point selection, the robust geometric descriptor, and the determining and optimization of correspondences based on key point spatial relationship. The loop voxel filtering filters point clouds to a specified resolution. We propose a key point selection algorithm which has a better anti-noise and fast ability. The feature descriptor of key points is highly exclusive which is based on the geometric relationship between the neighborhood points and the center of gravity of the neighborhood. The correspondences in the pair of two point clouds are determined according to the combined features of key points. Finally, the singular value decomposition and ICP algorithm are applied to align two point clouds. The proposed registration method can accurately and quickly register point clouds of different resolutions in noisy situations. We validate our proposal by presenting a quantitative experimental comparison with state-of-the-art methods. Experimental results show that the proposed point cloud registration algorithm has faster calculation speed, higher registration accuracy, and better anti-noise performance.

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Registration of Color Point Cloud by Combining with Color Moments Information

Cited by 4 publications

References 15 publications

Color Point Cloud Registration Based on Supervoxel Correspondence

Color Point Cloud Registration Based on Supervoxel Correspondence

Coarse registration of color point clouds by combining color feature and geometric feature

Automatic point cloud registration algorithm based on the feature histogram of local surface

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