2022
DOI: 10.1109/jstars.2022.3151699
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A Novel 3-D Local DAISY-Style Descriptor to Reduce the Effect of Point Displacement Error in Point Cloud Registration

Abstract: 3D point clouds are widely considered for applications in different fields. Various methods have been proposed to generate point cloud data: LIDAR and image matching from static and mobile platforms, including, e.g., Terrestrial Laser S canning (TLS ). With multiple point clouds from stationary platforms, point cloud registration is a crucial and fundamental issue. A standard approach is a point-based registration, which relies on pairs of corresponding points in twopoint clouds. Therefore, a necessary step in… Show more

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Cited by 16 publications
(8 citation statements)
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“…The RANSAC algorithm was applied to calculate the transformation. Ghorbani et al [41] extracted the tree positions to perform the registration of the forest point clouds. The geometric constraints were used to facilitate the RANSAC algorithm for computing the transformation.…”
Section: Specific Object-based Registration Methodsmentioning
confidence: 99%
“…The RANSAC algorithm was applied to calculate the transformation. Ghorbani et al [41] extracted the tree positions to perform the registration of the forest point clouds. The geometric constraints were used to facilitate the RANSAC algorithm for computing the transformation.…”
Section: Specific Object-based Registration Methodsmentioning
confidence: 99%
“…We compare the proposed HPNVD method with some leading approaches. Specifically, we select three effective LRF methods from the past five years for comparison, namely the LRF methods proposed by Zhou et al [18], Sun et al [16], and Ghorbani et al [42]. For fair comparison, the support radius for computing all the LRFs is uniformly set to 15pr, where pr represents the point cloud resolution.…”
Section: Parameter Settingsmentioning
confidence: 99%
“…For fair comparison, the support radius for computing all the LRFs is uniformly set to 15pr, where pr represents the point cloud resolution. The local feature descriptors included in the comparison are HGND [18], WHI [16], TOLDI [10], RoPS [5], FPFH [4], SHOT [8], and the method proposed by Ghorbani et al [42]. The parameter settings for all the descriptors used in the experiments are shown in the Table 1.…”
Section: Parameter Settingsmentioning
confidence: 99%
“…Several methods have been proposed for detecting 3D keypoints in point clouds, such as SIFT [24], local surface patches (LSP) [25], intrinsic shape signatures (ISS) [26], Histogram of Normal Orientations (HoNO) [27], and Uniform and Competency-Based 3D Keypoint Detector [28]. In addition to the aforementioned keypoint detection methods, 3D descriptors such as FPFH [29], SHOT [30], ROPS [31], binary shape context [32], and 3D DAISY [33] are used to describe these keypoints and are utilized in the process of matching. On the other hand, some methods extract linear features [34]- [36] or planar features [37]- [39] as features from point clouds and use them in the process of registration.…”
Section: A Related Workmentioning
confidence: 99%