Surface-based matching of 3D point clouds with variable coordinates in source and target system

Ge, Xuming; Wunderlich, Thomas

doi:10.1016/j.isprsjprs.2015.11.001

Cited by 52 publications

(29 citation statements)

References 37 publications

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“…For instance, the intersection lines of neighboring planes (Stamos and Leordeanu, 2003), 3D straight-lines (Habib et al, 2005, Al-Durgham andHabib, 2013), and spatial curves (Yang and Zang, 2014) are used as matching primitives. Whereas, plane correspondences (Dold and Brenner, 2006, Von Hansen, 2006, Xiao et al, 2012 and surface correspondences (Ge and Wunderlich, 2016) are frequently used as geometric primitives for alignment as well. Compared with the point-based methods, both lineor plane-based methods require abundant linear objects or smooth surfaces as candidates, which largely depends on the content of the scanned scenes.…”

Section: Related Workmentioning

confidence: 99%

Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

Boerner

Yao

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

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Section: Related Workmentioning

confidence: 99%

Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

Boerner

Yao

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For the primitive-based approaches, instead of using points, the geometric primitives formed by points (e.g., lines (Habib et al, 2005), planes (Xiao et al, 2013), or surfaces (Ge and Wunderlich, 2016)) are adopted as geometric features for the registration. Theoretically, the use of higher level geometric features can increase the robustness of identifying corresponding feature pairs.…”

Section: Related Workmentioning

confidence: 99%

“…For examples, the intersecting lines of neighbouring planes (Stamos and Leordeanu, 2003), 3D straight-lines (Habib et al, 2005) , and spatial curves (Yang and Zang, 2014) are used as matching primitives. Plane correspondences (Dold and Brenner, 2006;Von Hansen, 2006;Xiao et al, 2012) and surface correspondences (Ge and Wunderlich, 2016) are frequently used as geometric primitives for alignment as well. Compared with the pointbased methods both line-or plane-based methods require abundant linear objects or smooth surfaces as candidates which largely depends on the content of scanned scenes.…”

Section: Related Workmentioning

confidence: 99%

Dem Based Registration of Multi-Sensor Airborne Point Clouds Exemplary Shown on a River Side in Non Urban Area

Boerner¹,

Xu²,

Hoegner³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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This paper presents a method to register photogrammetric point clouds generated from optical images acquired by UAV and aerial LIDAR point clouds. Normally, the registration of two airborne scans of the same scene is solved by the use of control points and the direct registration using GNSS and INS information. However, the registration of multi-sensor point clouds without control points is more complicated and challenging. For the scene of non urban areas, the registration task gets even more complicated, because it is hard to extract sufficient geometric primitives from the building structures. For our proposed method, an outdoor scene is tested providing almost no man-made objects. Therefore, it is nearly impossible to search for planar objects and use them for registration. With no geometric primitives extracted, the proposed method utilizes the structure of the 2.5D DEM created from the ground points of the point cloud. Besides, instead of using control points or key points, the method automatic detect key planes from the 2.5D DEM as correspondences. These key planes are detected on a regular grid by the use of a predefined mask. To mark a DEM grid cell as key plane the histogram of sums of the angles between the center cell is used. Afterwards, similarity values between two key planes are calculated based on the histogram differences and a RANSAC based strategy is adopted to find corresponding key planes and estimate the transformation parameters. Experiments conducted in this paper indicate that it is feasible to register multi sensor point clouds with a big difference in their ground sampling distances with respect to the used cell size of the 2.5D DEM.

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“…For the dense and accurate 3D scene analysis and interpretation, especially in the context of urban areas, plenty of algorithms and approaches have been developed for a wide variety of applications such as semantic interpretation (Weinmann et al, 2015, Landrieu and Simonovsky, 2017, Vosselman et al, 2017, segmentation (Rabbani et al, 2006, Vo et al, 2015, registration (Aiger et al, 2008, Yang and Zang, 2014, Ge and Wunderlich, 2016, Theiler et al, 2014, object recognition (Schnabel et al, 2007, Yao et al, 2011, Niemeyer et al, 2014, Aldoma et al, 2012, Yu et al, 2016. For any proposed algorithms and methods, satisfying experiments and convincing evaluations are always non-trivial and crucial steps to validate the feasibility and performance of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

Sun

Boerner

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line-and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

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Surface-based matching of 3D point clouds with variable coordinates in source and target system

Cited by 52 publications

References 37 publications

Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

Dem Based Registration of Multi-Sensor Airborne Point Clouds Exemplary Shown on a River Side in Non Urban Area

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

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