Scale alignment of 3D point clouds with different scales

Lin, Baowei; Tamaki, Toru; Zhao, Fangda; Raytchev, Bisser; Kaneda, Kazufumi; Ichii, Koji

doi:10.1007/s00138-014-0633-2

Cited by 11 publications

(15 citation statements)

References 19 publications

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“…The scale estimation methods could be divided into two groups [15]: the first one is to directly estimate the scale ratio of the two point clouds; and the second one is to estimate the scale for each point cloud. In this paper, a key-scale rough estimation approach based on spin images and cumulative contribution rate of PCA has been used for registration of SfM and TLS point clouds.…”

Section: Key-scales Rough Estimation For Sfm and Tls Key Point Cloudsmentioning

confidence: 99%

A G-Super4PCS Registration Method for Photogrammetric and TLS Data in Geology

Zhang

Liu

et al. 2017

IJGI

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Photogrammetry and Terrestrial laser scanning (TLS) are the two primary non-contact active measurement techniques in geology. Integrating TLS data with digital images may achieve complementary advantages of spatial information as well as spectrum information, which would be very valuable for automatic rock surface extraction. In order to extract accurate and comprehensive geological information with both digital images and TLS point cloud, the registration problem for different sensor sources should be solved first. This paper presents a Generalized Super 4-points Congruent Sets (G-Super4PCS) algorithm to register the TLS point cloud as well as Structure from Motion (SfM) point cloud generated from disordered digital images. The G-Super4PCS algorithm mainly includes three stages: (1) key-scale rough estimation for point clouds; (2) extraction for the generalized super 4-points congruent base set and scale adaptive optimization; and (3) fine registration with Iterative Closest Point (ICP) algorithm. The developed method was tested with the columnar basalt data acquired in Guabushan National Geopark in Jiangsu Province, China. The results indicate that the proposed method could be used for indirect registration between digital images and TLS point cloud, and the result of which would be prepared for further integration research.

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Section: Key-scales Rough Estimation For Sfm and Tls Key Point Cloudsmentioning

confidence: 99%

A G-Super4PCS Registration Method for Photogrammetric and TLS Data in Geology

Zhang

Liu

et al. 2017

IJGI

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“…While independent of scaling, the method relies on similar geometries, and it can be applied only on continuous surfaces or extremely dense point clouds. A similar approach, that combines local descriptors at different scale to extract and compare the socalled keyscale of a model has been recently proposed by Lin et al [28]. The goal of this method is to estimate the relative scale only, so that ICP can be applied to find the alignment.…”

Section: Multi-modal Geometry Registrationmentioning

confidence: 99%

“…Recently, Mellado et al have proposed a technique called Growing Least Squares (GLS) [5], which aims at extending the scale-space formalism to point-set surfaces using implicit kernels evaluated at growing scales. This approach is computed locally at any location on the object, does not require any parametrization, supports arbitrary scale Differences in ... BBox fitting Game-Theoretic framework [26] SIFT [17], [18] Corsini et al [27] Keyscale [28] Our method…”

Section: Multi-scale Geometry Analysismentioning

confidence: 99%

See 1 more Smart Citation

Relative Scale Estimation and 3D Registration of Multi-Modal Geometry Using Growing Least Squares

Mellado

Dellepiane

Scopigno

2016

IEEE Trans. Visual. Comput. Graphics

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The advent of low cost scanning devices and the improvement of multi-view stereo techniques have made the acquisition of 3D geometry ubiquitous. Data gathered from different devices, however, result in large variations in detail, scale, and coverage. Registration of such data is essential before visualizing, comparing and archiving them. However, state-of-the-art methods for geometry registration cannot be directly applied due to intrinsic differences between the models, e.g., sampling, scale, noise. In this paper we present a method for the automatic registration of multi-modal geometric data, i.e., acquired by devices with different properties (e.g., resolution, noise, data scaling). The method uses a descriptor based on Growing Least Squares, and is robust to noise, variation in sampling density, details, and enables scale-invariant matching. It allows not only the measurement of the similarity between the geometry surrounding two points, but also the estimation of their relative scale. As it is computed locally, it can be used to analyze large point clouds composed of millions of points. We implemented our approach in two registration procedures (assisted and automatic) and applied them successfully on a number of synthetic and real cases. We show that using our method, multi-modal models can be automatically registered, regardless of their differences in noise, detail, scale, and unknown relative coverage.

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“…Pechenin et al [10] proposed and confirmed the method of ICP algorithm accuracy improvement based on the solution of a multi-objective optimization problem used for identification of location deviation parameters for profiles and surfaces with shaped form of surface. Baowei Lin, et al [11] proposed a scale alignment method of 3D point clouds with different scales which using a concept similar to ICP for directly estimating the scale ratio of two point clouds ,which is used to estimate the scale ratio for ICP methods. The proposed method works well both for simple and difficult point cloud datasets.…”

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confidence: 99%