Automatic coarse registration of point clouds using plane contour shape descriptor and topological graph voting

2021 ◽  
pp. 104055
Author(s):  
Pengcheng Wei ◽  
Li Yan ◽  
Hong Xie ◽  
Ming Huang
Keyword(s):  
Point Clouds ◽  
Shape Descriptor ◽  
Topological Graph ◽  
Contour Shape ◽  
Coarse Registration

scholarly journals An Efficient Probabilistic Registration Based on Shape Descriptor for Heritage Field Inspection

2020 ◽  
Vol 9 (12) ◽  
pp. 759
Author(s):  
Yufu Zang ◽  
Bijun Li ◽  
Xiongwu Xiao ◽  
Jianfeng Zhu ◽  
Fancong Meng
Keyword(s):  
Cultural Heritage ◽  
Solution Space ◽  
Point Clouds ◽  
Shape Descriptor ◽  
Free Form ◽  
Registration Method ◽  
Field Inspection ◽  
3D Point Clouds ◽  
Quality Issues ◽  
Cultural Heritage Objects

Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches.

scholarly journals Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds

2014 ◽  
Vol XL-5 ◽  
pp. 629-632 ◽  
Author(s):  
Y. Yu ◽  
J. Li ◽  
H. Guan ◽  
D. Zai ◽  
C. Wang
Keyword(s):  
Point Cloud ◽  
Euclidean Distance ◽  
Point Clouds ◽  
Shape Descriptor ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Automated Extraction ◽  
Normalized Cut ◽  
Model Driven ◽  
Automated Algorithm

This paper presents an automated algorithm for extracting 3D trees directly from 3D mobile light detection and ranging (LiDAR) data. To reduce both computational and spatial complexities, ground points are first filtered out from a raw 3D point cloud via blockbased elevation filtering. Off-ground points are then grouped into clusters representing individual objects through Euclidean distance clustering and voxel-based normalized cut segmentation. Finally, a model-driven method is proposed to achieve the extraction of 3D trees based on a pairwise 3D shape descriptor. The proposed algorithm is tested using a set of mobile LiDAR point clouds acquired by a RIEGL VMX-450 system. The results demonstrate the feasibility and effectiveness of the proposed algorithm.

scholarly journals COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

2016 ◽  
Vol III-5 ◽  
pp. 97-103 ◽  
Author(s):  
Jinhu Wang ◽  
Roderik Lindenbergh ◽  
Yueqian Shen ◽  
Massimo Menenti
Keyword(s):  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Data Sampling ◽  
Surface Geometry ◽  
Registration Method ◽  
Input Point ◽  
Coarse Registration ◽  
Point Distance ◽  
Fine Registration

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

scholarly journals ROBUST AND ACCURATE PLANE SEGMENTATION FROM POINT CLOUDS OF STRUCTURED SCENES

2020 ◽  
Vol V-2-2020 ◽  
pp. 221-226
Author(s):  
P. Hu ◽  
Y. Liu ◽  
M. Tian ◽  
M. Hou
Keyword(s):  
Energy Minimization ◽  
Point Cloud ◽  
Point Clouds ◽  
Segmentation Method ◽  
Topological Graph ◽  
New Approach ◽  
Input Point ◽  
Planar Surfaces ◽  
Point Density ◽  
Efficiency And Effectiveness

Abstract. Plane segmentation from the point cloud is an important step in various types of geo-information related to human activities. In this paper, we present a new approach to accurate segment planar primitives simultaneously by transforming it into the best matching issue between the over-segmented super-voxels and the 3D plane models. The super-voxels and its adjacent topological graph are firstly derived from the input point cloud as over-segmented small patches. Such initial 3D plane models are then enriched by fitting centroids of randomly sampled super-voxels, and translating these grouped planar super-voxels by structured scene prior (e.g. orthogonality, parallelism), while the generated adjacent graph will be updated along with planar clustering. To achieve the final super-voxels to planes assignment problem, an energy minimization framework is constructed using the productions of candidate planes, initial super-voxels, and the improved adjacent graph, and optimized to segment multiple consistent planar surfaces in the scenes simultaneously. The proposed algorithms are implemented, and three types of point clouds differing in feature characteristics (e.g. point density, complexity) are mainly tested to validate the efficiency and effectiveness of our segmentation method.

scholarly journals 3D Shape Completion with Multi-View Consistent Inference

2020 ◽  
Vol 34 (07) ◽  
pp. 10997-11004 ◽  
Author(s):  
Tao Hu ◽  
Zhizhong Han ◽  
Matthias Zwicker
Keyword(s):  
Point Clouds ◽  
Shape Descriptor ◽  
Depth Image ◽  
Regularization Term ◽  
3D Shape ◽  
Depth Images ◽  
3D Point Clouds ◽  
Geometric Consistency ◽  
Shape Completion ◽  
Data Term

3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been presented. These methods represent shapes as multiple depth images, which can be back-projected to yield corresponding 3D point clouds, and they perform shape completion by learning to complete each depth image using neural networks. While view-based methods lead to state-of-the-art results, they currently do not enforce geometric consistency among the completed views during the inference stage. To resolve this issue, we propose a multi-view consistent inference technique for 3D shape completion, which we express as an energy minimization problem including a data term and a regularization term. We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor. Experimental results demonstrate that our method completes shapes more accurately than previous techniques.

scholarly journals Efficient Coarse Registration of Pairwise TLS Point Clouds Using Ortho Projected Feature Images

2020 ◽  
Vol 9 (4) ◽  
pp. 255
Author(s):  
Hua Liu ◽  
Xiaoming Zhang ◽  
Yuancheng Xu ◽  
Xiaoyong Chen
Keyword(s):  
Parameter Estimation ◽  
Degrees Of Freedom ◽  
High Efficiency ◽  
Point Clouds ◽  
Azimuth Angle ◽  
Translation Vector ◽  
Scale Invariant ◽  
Registration Method ◽  
Coarse Registration ◽  
Vertical Translation

The degree of automation and efficiency are among the most important factors that influence the availability of Terrestrial light detection and ranging (LiDAR) Scanning (TLS) registration algorithms. This paper proposes an Ortho Projected Feature Images (OPFI) based 4 Degrees of Freedom (DOF) coarse registration method, which is fully automated and with high efficiency, for TLS point clouds acquired using leveled or inclination compensated LiDAR scanners. The proposed 4DOF registration algorithm decomposes the parameter estimation into two parts: (1) the parameter estimation of horizontal translation vector and azimuth angle; and (2) the parameter estimation of the vertical translation vector. The parameter estimation of the horizontal translation vector and the azimuth angle is achieved by ortho projecting the TLS point clouds into feature images and registering the ortho projected feature images by Scale Invariant Feature Transform (SIFT) key points and descriptors. The vertical translation vector is estimated using the height difference of source points and target points in the overlapping regions after horizontally aligned. Three real TLS datasets captured by the Riegl VZ-400 and the Trimble SX10 and one simulated dataset were used to validate the proposed method. The proposed method was compared with four state-of-the-art 4DOF registration methods. The experimental results showed that: (1) the accuracy of the proposed coarse registration method ranges from 0.02 m to 0.07 m in horizontal and 0.01 m to 0.02 m in elevation, which is at centimeter-level and sufficient for fine registration; and (2) as many as 120 million points can be registered in less than 50 s, which is much faster than the compared methods.

Verfahren zur Registrierung von Laserscannerdaten in Waldbeständen | Methods for registration laser scanner point clouds in forest stands

2011 ◽  
Vol 162 (6) ◽  
pp. 178-185 ◽  
Author(s):  
Anne Bienert ◽  
Katharina Pech ◽  
Hans-Gerd Maas
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Forest Stand ◽  
Geometric Transformation ◽  
Complex Object ◽  
Tree Detection ◽  
Airborne Laser ◽  
Coarse Registration ◽  
Fine Registration

Laser scanning is a fast and efficient 3-D measurement technique to capture surface points describing the geometry of a complex object in an accurate and reliable way. Besides airborne laser scanning, terrestrial laser scanning finds growing interest for forestry applications. These two different recording platforms show large differences in resolution, recording area and scan viewing direction. Using both datasets for a combined point cloud analysis may yield advantages because of their largely complementary information. In this paper, methods will be presented to automatically register airborne and terrestrial laser scanner point clouds of a forest stand. In a first step, tree detection is performed in both datasets in an automatic manner. In a second step, corresponding tree positions are determined using RANSAC. Finally, the geometric transformation is performed, divided in a coarse and fine registration. After a coarse registration, the fine registration is done in an iterative manner (ICP) using the point clouds itself. The methods are tested and validated with a dataset of a forest stand. The presented registration results provide accuracies which fulfill the forestry requirements.

scholarly journals A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds

2016 ◽  
Vol 8 (3) ◽  
pp. 189 ◽  
Author(s):  
Meizhang He ◽  
Qing Zhu ◽  
Zhiqiang Du ◽  
Han Hu ◽  
Yulin Ding ◽  
...  
Keyword(s):  
Point Clouds ◽  
Shape Descriptor ◽  
Airborne Lidar ◽  
3D Shape

scholarly journals RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings

2021 ◽  
Vol 13 (10) ◽  
pp. 1882
Author(s):  
Yijie Wu ◽  
Jianga Shang ◽  
Fan Xue
Keyword(s):  
Degrees Of Freedom ◽  
Low Cost ◽  
Point Clouds ◽  
Data Sets ◽  
Digital Twin ◽  
3D Point Clouds ◽  
Coarse Registration ◽  
Smartphone Sensors ◽  
Novel Method ◽  
Twin Buildings

Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named `Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.

Sign in / Sign up

Export Citation Format

Share Document