Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets

Xu, Yangsheng; Boerner, R.; Yao, Wei; Hoegner, L.; Stilla, Uwe

doi:10.1016/j.isprsjprs.2019.02.015

Cited by 99 publications

(54 citation statements)

References 45 publications

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“…For instance, they are not effective for sparse point clouds, require good approximate values to avoid convergence to weak local minima, they are sensitive to noise, and involve a matching step which incurs a high computational cost. Furthermore, when a downsampling step is applied, as in [6], the details of objects in the scene may be lost [12]. In contrast, closed-form solutions estimate the transformation parameters in one-step and they do not require approximate values.…”

Section: Related Workmentioning

confidence: 99%

“…Pairwise registration involves finding feature correspondences between pairs of point clouds and minimizing the sum of residuals over all such feature correspondences for the estimation of transformation parameters (3D rotation matrix and 3D translation vector), which establish the relative orientation for each pair of scans in a common coordinate system. In practice, pairwise registration using free-form correspondences (e.g., iterative closest point algorithm [1]), feature point-based (e.g., keypoints) methods [2][3][4][5][6][7], or primitive-based (e.g., lines or planar surfaces) approaches [8][9][10][11][12][13] should be applied first to obtain the transformation parameters. However, a problem which arises in the registration of multiple point clouds is that corresponding scan features may still present significant residual errors from pairwise registration task.…”

Section: Introductionmentioning

confidence: 99%

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Global Registration of Terrestrial Laser Scanner Point Clouds Using Plane-to-Plane Correspondences

2020

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Registration of point clouds is a central problem in many mapping and monitoring applications, such as outdoor and indoor mapping, high-speed railway track inspection, heritage documentation, building information modeling, and others. However, ensuring the global consistency of the registration is still a challenging task when there are multiple point clouds because the different scans should be transformed into a common coordinate frame. The aim of this paper is the registration of multiple terrestrial laser scanner point clouds. We present a plane-based matching algorithm to find plane-to-plane correspondences using a new parametrization based on complex numbers. The multiplication of complex numbers is based on analysis of the quadrants to avoid the ambiguity in the calculation of the rotation angle formed between normal vectors of adjacent planes. As a matching step may contain several matrix operations, our strategy is applied to reduce the number of mathematical operations. We also design a novel method for global refinement of terrestrial laser scanner data based on plane-to-plane correspondences. The rotation parameters are globally refined using operations of quaternion multiplication, while the translation parameters are refined using the parameters of planes. The global refinement is done non-iteratively. The experimental results show that the proposed plane-based matching algorithm efficiently finds plane correspondences in partial overlapping scans providing approximate values for the global registration, and indicate that an accuracy better than 8 cm can be achieved by using our global fine plane-to-plane registration method.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global Registration of Terrestrial Laser Scanner Point Clouds Using Plane-to-Plane Correspondences

2020

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“…In addition, Mellado N et al [17] greatly enhanced the 4PCS algorithm by smart indexing. Voxel-based 4PCS [18] voxelizes the point cloud and generates plane patches before extracting four-plane congruent sets. V4PCS improves the robustness to unequal point density or point clouds from different sources.…”

Section: Coarse Registrationmentioning

confidence: 99%

Hierarchical Optimization of 3D Point Cloud Registration

Liu

Zhang

Xie

et al. 2020

Sensors

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Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.

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“…One of the most effective point clouds registration algorithms based on RANSAC is Four Point Congruent Set (4PCS) by Aiger et al [1]. Based on this idea, Voxel Four Plane Congruent Set (V4PCS) was proposed by Xu et al [10]. The major innovation of V4PCS is to replace the 4-point bases in 4PCS by the 4-plane bases.…”

Section: Related Workmentioning

confidence: 99%

Urban Night Scenery Reconstruction by Day-night Registration and Synthesis

Dai

Meger

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Although large-scale 3D reconstruction by photogrammetry has been well studied and applied, the reconstruction of night scenery in urban areas has not been thoroughly considered. At night, low-light conditions often cause the images to lack sharpness and high-dynamic range issue leads to saturation. The SFM reconstruction pipeline that works well in daylight is likely to recover only limited dense points of bright fragmented objects near artificial lighting. Here, we propose a novel solution based on registration and synthesis between the night-time reconstruction and that of the same region in daytime. A registration pipeline is developed for conformal matching of the day and night point clouds. For the coarse registration step, we use detected plane features to search and match 4-plane congruent sets. For the fine registration step, we consider the positions of windows, a commonly-occurring object cue in urban building scenes as markers for accurate positioning. This leads to final registration error less than 0.2 degrees in rotation, and 0.2% in scale and translation. Finally, we synthesize the daytime textured model and the night point clouds to produce vivid visual effects of urban night scenery.

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Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets

Cited by 99 publications

References 45 publications

Global Registration of Terrestrial Laser Scanner Point Clouds Using Plane-to-Plane Correspondences

Global Registration of Terrestrial Laser Scanner Point Clouds Using Plane-to-Plane Correspondences

Hierarchical Optimization of 3D Point Cloud Registration

Urban Night Scenery Reconstruction by Day-night Registration and Synthesis

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