Pairwise Registration Research Articles

Pairwise coarse registration of point clouds by traversing voxel-based 2-plane bases

ABSTRACT To obtain the complete coverage when scanning a large-scale scene using a laser scanner, registration of pairwise point clouds by the corresponding geometric features is usually necessary under the condition of insufficient direct geographic reference information of GNSS sensors. In this paper, we propose a target-less coarse registration method for pairwise point clouds using the corresponding voxel-based planes, which are identified by a pair of conjugate 2-plane bases. The voxel-based planes are firstly extracted from the entire point cloud with 3D cubic grids decomposed by the octree-based voxelization; Then, the 2-plane bases in each point cloud are constructed, and by comparing the dihedral angles of two 2-plane bases that come from the source and target point clouds, respectively, the conjugate 2-plane base pairs are generated one by one; Next, a set of plane correspondences is identified by a pair of conjugate 2-plane bases, and its corresponding largest consistency planes (LCP) set is calculated; Finally, a series of plane correspondence sets are obtained using the generated pairs of conjugate 2-plane bases, and the one with the highest LCP is used to compute the transformation matrix between the pairwise point clouds. Experimental results revealed that our proposed pairwise coarse registration method can be effective for aligning point clouds acquired from indoor and outdoor scenes, with rotation errors less than 0.4 degrees, translation errors less than 0.4 m, root mean square distance (RMSD) less than 0.42 m, and successful registration rate (SRR) about 98%. Furthermore, our proposed method was more efficient than the point- and line-based methods under the same hardware and software configuration conditions.

Point set registration via rigid transformation consensus

Point set registration is a fundamental problem in many domains. This paper proposes a novel pair-wise registration algorithm based on the rigid transformation consensus. It starts by building a point correspondence set, which contains both inliers and outliers. Due to non-overlapping regions, it associates each point correspondence with a latent variable and formulates pair-wise registration as a maximum likelihood estimation problem, which is optimized by the expectation-maximum algorithm. Since all inliers follows the consensus of one similar rigid transformation, each correspondence is assigned a posterior probability to indicate whether it is inlier or outlier. To obtain the desired result, it requires to alternatively implement the establishment of point correspondence and maximum likelihood estimation. Given initial rigid transformation, the proposed algorithm is able to obtain a desired registration result for the pair-wise registration. Experiments tested on public available data sets illustrate its superior performance on accuracy and efficiency over previous algorithms.

Pairwise versus Transitive Inverse Consistent Longitudinal Rigid Registration of Magnetic Resonance Images of Athletes With Repetitive Non-Concussive Head Injuries: Effects on Regional Distributions of Diffusion Measures.

Accurate alignment of longitudinal diffusion weighted imaging (DWI) scans of a subject is necessary to investigate longitudinal changes in DWI-derived diffusion measures such as fractional anisotropy (FA), mean diffusivity (MD), and quantitative anisotropy (QA). Currently, studies investigating these changes in the context of repetitive non-concussive head injuries (RHIs) perform pairwise rigid registration of all scans of a subject to the first scan or any other reference scan or template. Prajapati et.al 1 show that this strategy of performing pairwise rigid registration lead to a discrepancy in the rigid transformations. To eliminate this discrepancy, they propose performing transitive inverse consistent rigid registration of the longitudinal scans, and they analyze the impact of this approach on the mean values of the local/regional estimates of these diffusion measures. In this work, we further analyze the impact of transitive inverse consistent rigid registration on the distributions (CDFs) of the local/regional estimates of diffusion measures. We identify the regions (among the 48 anatomically defined regions by the JHU DTI-based white matter atlas2,3) that show significant differences in the CDFs obtained using pairwise inverse consistent and transitive inverse consistent rigid registration by performing the two sided Kolmogorov-Smirnov(KS) hypothesis test. We find that for MD and QA, there are certain subjects that have five or more regions with significant differences in the CDFs. Further, these are the same subjects for which Prajapati et.al 1 found regions with 2%-4% differences in the mean values of these diffusion measures. Thus, our results further strengthen the recommendation made by Prajapati et.al 1 to employ transitive inverse consistent rigid registration when investigating local/regional longitudinal changes in diffusion measures.

High-accuracy point cloud registration for 3D shape measurement based on double constrained intersurface mutual projections

The accuracy of 3D shape measurement directly determines the quality and reliability of intelligent manufacturing, and point cloud registration is the key factor. However, the dislocation of discrete point clouds in the overlapping area seriously reduces the registration accuracy. This paper proposes a new high-accuracy registration method based on double constrained intersurface mutual projections. First, the initial registration set is built by mutual projections between similar local regions, then the final registration set is determined by the rigid transformation consistency constraint, finally, high-accuracy pairwise registration is realized. On this basis, the K-means clustering is further fused to achieve multiview global optimization. On Stanford dataset, both of the pairwise and multiview registration error significantly decreased. In the experiment with GoScanG1 scanner, the surface error of pairwise registration was reduced by 2.2%; that of the multiview registration was reduced by 42.5%, which shows the advancement of the new method in actual measurements.

Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans

We present the Regensburg Breast Shape Model (RBSM)—a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish dense correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. A major challenge when modeling female breasts from surface-only 3D breast scans is the non-separability of breast and thorax. In order to weaken the strong coupling between breast and surrounding areas, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet effective strategy significantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of different breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a specificity of 2.8 mm. To underline the expressiveness of the proposed model, we finally demonstrate in two showcase applications how the RBSM can be used for surgical outcome simulation and the prediction of a missing breast from the remaining one. Our model is available at https://www.rbsm.re-mic.de/.

3D Point Set Registration based on Hierarchical Descriptors

Registering partial point clouds is crucial in numerous applications in the field of robotics, vision, and graphics. For arbitrary configurations, the registration problem requires an initial global alignment, which is computationally expensive and often still requires refinement. In this paper, we propose a pair-wise global registration method that combines the fast convergence made possible by global hierarchical surface descriptors with the arbitrarily fine sampling enabled by continuous surface representations. Registration is performed by matching descriptors of increasing resolution – which the continuous surfaces allow us to choose arbitrarily high – while restricting the search space according to the hierarchy. We evaluated our method on a large set of pair-wise registration problems, demonstrating very competitive registration accuracy that often makes subsequent refinement with a local method unnecessary.

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas.

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.

Automatic Pairwise Coarse Registration of Terrestrial Point Clouds Using 3D Line Features

The fully automatic registration of 3D terrestrial laser scanning (TLS) point clouds, which is the first step in the usage of point clouds, is a highly challenging task in Light Detection and Ranging (LiDAR) remote sensing applications. Here, an automatic algorithm for pairwise coarse registration of TLS point clouds using 3D line features is proposed. First, the 3D line sets were extracted from the original pair of point clouds respectively, and two arbitrary lines from a point cloud were used to construct a 2-line base; Then, a pair of conjugate 2-line bases were identified from the source and target 3D line sets at a time, based on which a 3D rotation matrix together with its corresponding overlap between the pairwise 3D line sets was calculated; Third, a series of 3D rotation matrixes together with their overlaps were obtained using the traversing strategy to identify conjugate 2-line base pairs, and the 3D rotation matrix with the highest overlap was outputted as the final 3D rotation matrix, which was further used to compute the corresponding lines set from the pairwise 3D line sets; Next, based on the set of common perpendicular midpoints between the line correspondences, the 3D translation vector was computed; Finally, the 3D transformation matrix between pairwise point clouds was obtained by combining the 3D rotation matrix and the 3D translation vector. The proposed method was tested on three different TLS datasets, with experimental results demonstrating that the proposed algorithm could perform well on registering pairwise TLS point clouds. The rotation and translation errors of aligning the nine experimental pairwise point clouds were all less than 0.50° and 0.55m, respectively. This registration framework was also shown to be superior to state-of-the-art methods in terms of registration accuracy.

Raw Scanned Point Cloud Registration with Repetition for Aircraft Fuel Tank Inspection

The aircraft fuel tank, as the source of fuel supply, is one of the key components for an aircraft. It is fastened to the fuselage with thousands of rivets, in which each rivet is sealed carefully to prevent the leakage. Thus, the sealing quality of the aircraft fuel tank needs to be precisely measured to ensure its normal function. To achieve the measurement, the complete 3D point clouds captured by 3D scanning are usually utilized. Therefore, it is necessary to align the different views of the aircraft fuel tank point clouds, to obtain the complete data. However, the aircraft fuel tank consists of abundant repetitive structures, such as the regular-distributed rivets, which can lead to mismatches in the aircraft fuel tank point clouds if using the conventional registration methods. In order to address the challenges, we propose a new registration framework, which is able to handle the point cloud match under the repetitive and complex scenes. First, a novel 3D descriptor called LP-PPF is constructed with the point and line features from the aircraft fuel tank point clouds, so as to identify the repetitive structures correctly. Then, according to the proposed descriptor, the accurate registration can be achieved between the neighboring point cloud pairs. Finally, based on the results of pairwise registration, the complete aircraft fuel tank point cloud can be obtained via the multi-view registration method. Experiments demonstrate that our method achieves favorable results on both synthetic and raw scanned point cloud data.

Lung-CRNet: A convolutional recurrent neural network for lung 4DCT image registration.

Deformable image registration (DIR) of lung four-dimensional computed tomography (4DCT) plays a vital role in a wide range of clinical applications. Most of the existing deep learning-based lung 4DCT DIR methods focus on pairwise registration which aims to register two images with large deformation. However, the temporal continuities of deformation fields between phases are ignored. This paper proposes a fast and accurate deep learning-based lung 4DCT DIR approach that leverages the temporal component of 4DCT images. We present Lung-CRNet, an end-to-end convolutional recurrent registration neural network for lung 4DCT images and reformulate 4DCT DIR as a spatiotemporal sequence predicting problem in which the input is a sequence of three-dimensional computed tomography images from the inspiratory phase to the expiratory phase in a respiratory cycle. The first phase in the sequence is selected as the only reference image and the rest as moving images. Multiple convolutional gated recurrent units (ConvGRUs) are stacked to capture the temporal clues between images. The proposed network is trained in an unsupervised way using a spatial transformer layer. During inference, Lung-CRNet is able to yield the respective displacement field for each reference-moving image pair in the input sequence. We have trained the proposed network using a publicly available lung 4DCT dataset and evaluated performance on the widely used the DIR-Lab dataset. The mean and standard deviation of target registration error are 1.56 1.05 mm on the DIR-Lab dataset. The computation time for each forward prediction is less than 1 s on average. The proposed Lung-CRNet is comparable to the existing state-of-the-art deep learning-based 4DCT DIR methods in both accuracy and speed. Additionally, the architecture of Lung-CRNet can be generalized to suit other groupwise registration tasks which align multiple imagessimultaneously.

GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene

Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.

Triple-Input-Unsupervised neural Networks for deformable image registration

• The TIUNet is the first medical image registration CNN with triple-input structure. • Three kinds of loss functions are conducted to supporting the novel structure. • The deformation consistency of two registration results is novelly considered. • The training data could be naturally augmented with the triple-input structure. Deep-learning frameworks have been widely used in deformable image registration(DIR), which is a fundamental step in medical image analysis. In traditional deep-learning-based DIR methods, pairwise registration is conducted for each image, cooperated with a dual-input structure. They only considered that the registered images need to be as similar as possible to the target image, while ignored that all registered images to the same target image should have similar appearance too. Therefore, in this paper we add the deformation constraints between different registered images to the registration network in order to enhance the appearance consistency of the registered images and improve the registration accuracy. Specifically, a Triple-Input-Unsupervised neural Network (TIUNet) is proposed to register two source images to a common target image at the same time. Then, a novel deformation-strengthened loss is designed to simulate the deformation constraint between the two warped source images. Finally, the deformation constraints between the source images and the target image are jointly optimized to obtain the deformation field model. The experimental results on the available tissue segmentation database demonstrate that our proposed TIUNet method shows a superior performance of deformation field model over several other advanced deformable image registration algorithms. Moreover, benefited from the triple-input structure, our proposed TIUNet method could expand the available training sets naturally and is suitable for the registration task with fewer training data.

Transitive Inverse Consistent Rigid Longitudinal Registration of Diffusion Weighted Magnetic Resonance Imaging: A Case Study in Athletes With Repetitive Non-Concussive Head Injuries.

Significant longitudinal changes in metrics derived from diffusion weighted magnetic resonance (MR) images of the brain have been observed in athletes subject to repetitive non-concussive head injuries (RHIs). Accurate alignment of longitudinal scans of a subject is an important step in detecting and quantifying these changes. Currently, tools such as DSI Studio [1], FreeSurfer [2], and FSL [3] perform pairwise rigid registration of all scans in a longitudinal sequence to the first time-point scan (or to another reference scan or template). While the rigid transformations obtained using this strategy can be computed in a manner that enforces inverse consistency, for the case of three or more scans, the transformations are not transitive. This can lead to discrepancy in the rigid transformations that can be measured in physical units. Using a diffusion MRI dataset collected and analyzed as part of a larger study in [4], [5], [6], we illustrate this discrepancy, and we show how it can lead to uncertainty in local/regional estimates of diffusion metrics including fractional anistropy (FA), mean diffusivity (MD), and quantitatve anisotropy (QA). Additionally, we propose a method to perform transitive longitudinal rigid registration of a sequence of scans in a manner that guarantees that the discrepancy in the transformations will be eliminated.Clinical relevance- This paper establishes that standard processing pipelines for performing longitudinal analysis of diffusion MR images of the brain exhibit registration discrepancies that can be eliminated.

Few-shot learning for deformable image registration in 4DCT images.

To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy. We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process. The proposed FR-Net is evaluated for 4D groupwise and 3D pairwise registration on thoracic 4DCT data sets DIR-Lab and POPI. FR-Net displays an averaged target registration error of 1.48 mm and 1.16 mm between the maximum inhalation and exhalation phases in the 4DCT of DIR-Lab and POPI, respectively, with approximately 2 min required to optimize one 4DCT. Overall, FR-Net outperforms state-of-the-art methods in terms of registration accuracy and exhibits a low computational time. We develop a few-shot groupwise DIR algorithm for 4DCT images. The promising registration performance and computational efficiency demonstrate the prospective applications of this approach in registration tasks for online adaptive radiotherapy. This work exploits DL models to solve the optimization problem in registering 4DCT scans while combining groupwise registration and few-shot learning strategy to solve the problem of consuming computational time and inferior registration accuracy.

Reduction of motion effects in myocardial arterial spin labeling.

To evaluate the accuracy and reproducibility of myocardial blood flow measurements obtained under different breathing strategies and motion correction techniques with arterial spin labeling. A prospective cardiac arterial spin labeling study was performed in 12 volunteers at 3 Tesla. Perfusion images were acquired twice under breath-hold, synchronized-breathing, and free-breathing. Motion detection based on the temporal intensity variation of a myocardial voxel, as well as image registration based on pairwise and groupwise approaches, were applied and evaluated in synthetic and in vivo data. A region of interest was drawn over the mean perfusion-weighted image for quantification. Original breath-hold datasets, analyzed with individual regions of interest for each perfusion-weighted image, were considered as reference values. Perfusion measurements in the reference breath-hold datasets were in line with those reported in literature. In original datasets, prior to motion correction, myocardial blood flow quantification was significantly overestimated due to contamination of the myocardial perfusion with the high intensity signal of blood pool. These effects were minimized with motion detection or registration. Synthetic data showed that accuracy of the perfusion measurements was higher with the use of registration, in particular after the pairwise approach, which probed to be more robust to motion. Satisfactory results were obtained for the free-breathing strategy after pairwise registration, with higher accuracy and robustness (in synthetic datasets) and higher intrasession reproducibility together with lower myocardial blood flow variability across subjects (in in vivo datasets). Breath-hold and synchronized-breathing after motion correction provided similar results, but these breathing strategies can be difficult to perform by patients.

On Bundle Adjustment for Multiview Point Cloud Registration

Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise registration and pose synchronization, the concept of Bundle Adjustment (BA) has been proven to better maintain global consistency. So in this work, we make the multiview point-cloud registration more tractable from a different perspective in resolving range-based BA. We first analyse the optimal condition of the objective function of BA that unifies some previous approaches. Based on this analysis, we propose an objective function that takes both measurement noises and computational cost into account. For the feature parameter update, instead of calculating the global distribution parameters from the raw measurements, we aggregate the local distributions in a frame-wise fashion at each iteration. The computational cost of feature update is then only dependent on the number of scans. Finally, we develop a multiview registration system using voxel-based quantization that can be applied in real-world scenarios. The experimental results demonstrate our superiority over the baselines in terms of both accuracy and speed. Moreover, the results also show that our average positioning errors achieve the centimeter level. Related materials are available at our project page https://hyhuang1995.github.io/bareg/.

A Closed-Form Solution to Linear Feature-Based Registration of LiDAR Point Clouds

Due to the high complexity of geo-spatial entities and the limited field of view of LiDAR equipment, pairwise registration is a necessary step for integrating point clouds from neighbouring LiDAR stations. Considering that accurate extraction of point features is often difficult without the use of man-made reflectors, and the initial approximate values for the unknown transformation parameters must be estimated in advance to ensure the correct operation of those iterative methods, a closed-form solution to linear feature-based registration of point clouds is proposed in this study. Plücker coordinates are used to represent the linear features in three-dimensional space, whereas dual quaternions are employed to represent the spatial transformation. Based on the theory of least squares, an error norm (objective function) is first constructed by assuming that each pair of corresponding linear features is equivalent after registration. Then, by applying the extreme value analysis to the objective function, detailed derivations of the closed-form solution to the proposed linear feature-based registration method are given step by step. Finally, experimental tests are conducted on a real dataset. The derived experimental result demonstrates the feasibility of the proposed solution: By using eigenvalue decomposition to replace the linearization of the objective function, the proposed solution does not require any initial estimates of the unknown transformation parameters, which assures the stability of the registration method.

A robust method for image stitching

We propose a novel method for large-scale image stitching that is robust against repetitive patterns and featureless regions in the imagery. In such cases, state-of-the-art image stitching methods easily produce image alignment artifacts, since they may produce false pairwise image registrations that are in conflict within the global connectivity graph. Our method augments the current methods by collecting all the plausible pairwise image registration candidates, among which globally consistent candidates are chosen. This enables the stitching process to determine the correct pairwise registrations by utilizing all the available information from the whole imagery, such as unambiguous registrations outside the repeating pattern and featureless regions. We formalize the method as a weighted multigraph whose nodes represent the individual image transformations from the composite image, and whose sets of multiple edges between two nodes represent all the plausible transformations between the pixel coordinates of the two images. The edge weights represent the plausibility of the transformations. The image transformations and the edge weights are solved from a non-linear minimization problem with linear constraints, for which a projection method is used. As an example, we apply the method in a large-scale scanning application where the transformations are primarily translations with only slight rotation and scaling component. Despite these simplifications, the state-of-the-art methods do not produce adequate results in such applications, since the image overlap is small, which can be featureless or repetitive, and misalignment artifacts and their concealment are unacceptable.

Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement.

In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.

Target-based automated matching of multiple terrestrial laser scans for complex forest scenes

Terrestrial laser scanners are widely used to derive unbiased and non-destructive estimates of the vertical distribution of the plant area index and plant area volume density at plot-level scales, as well as the above-ground biomass, height, and diameter at breast height of individual trees. Multiple scans are often employed to capture and register data so that all of the stems can be detected and their complete forms can be analyzed. Researchers have traditionally preferred target-less strategies to register scans because of their low cost and convenience. However, in complex forest scenes, even state-of-the-art approaches cannot guarantee the success of any pairwise registration. In this study, we present an automated target-based processing approach for the registration of unordered scans in complex forest scenes. In contrast to previous studies, the proposed registration method automatically detects the artificial targets and builds a geometric network to judge their connectivity. A pose graph is then exploited to combine these data with the corresponding pairwise transformation, and then the scans are integrated into a unified coordinate system. This method is more robust and efficient than target-less approaches because it is independent of the characteristics of individual trees and does not require ground information. In an experimental scenario, we use an extremely complex wild bamboo forest scene to evaluate the performance of the proposed approach in terms of robustness, accuracy, and efficiency.