Correspondence detection is a vital step in point cloud registration and it can help getting a reliable initial alignment. In this paper, we put forward an advanced point feature-based graph matching algorithm to solve the initial alignment problem of rigid 3D point cloud registration with partial overlap. Specifically, Fast Point Feature Histograms are used to determine the initial possible correspondences firstly. Next, a new objective function is provided to make the graph matching more suitable for partially overlapping point cloud. The objective function is optimized by the simulated annealing algorithm for final group of correct correspondences. Finally, we present a novel set partitioning method which can transform the NP-hard optimization problem into a O(n3)-solvable one. Experiments on the Stanford and UWA public data sets indicates that our method can obtain better result in terms of both accuracy and time cost compared with other point cloud registration methods.
KEYWORDS: Motion models, 3D modeling, Motion estimation, Rigid registration, Data modeling, Lithium, Distance measurement, 3D scanning, Fluctuations and noise, Sensors
Three-dimensional modeling of scene or object requires registration of multiple range scans, which are obtained by range sensor from different viewpoints. An approach is proposed for scaling registration of multiview range scans via motion averaging. First, it presents a method to estimate overlap percentages of all scan pairs involved in multiview registration. Then, a variant of iterative closest point algorithm is presented to calculate relative motions (scaling transformations) for these scan pairs, which contain high overlap percentages. Subsequently, the proposed motion averaging algorithm can transform these relative motions into global motions of multiview registration. In addition, it also introduces the parallel computation to increase the efficiency of multiview registration. Furthermore, it presents the error criterion for accuracy evaluation of multiview registration result, which can make it easy to compare results of different multiview registration approaches. Experimental results carried out with public available datasets demonstrate its superiority over related approaches.
To address the surface reconstruction issue, this paper proposes an efficient and accurate approach for registration of multiview range scans. It has a good objective function designed, where all multiview registration parameters are involved. To solve this function, the coarse-to-fine approach is proposed, where each range scan should be sequentially registered to a coarse surface model, which is reconstructed by other scans with initial multiview alignment. By applying the trimmed iterative closest point algorithm, it can sequentially obtain good multiview registration results for each scan, which can then be immediately utilized to refine the coarse surface model for registration of other scans. To acquire accurate surface model, several rounds of update should be applied to all range scans involved in the multiview registration. With the increase of update round, it can finally obtain the accurate surface model. Experimental results on public data sets illustrate its superiority over previous approaches.
The problem of registering point sets with outliers including noises and missing data is discussed in this paper. To solve this problem, a novel objective function is proposed by introducing an overlapping percentage for partial registration. Moreover, a novel robust iterative closest point (ICP) algorithm is proposed which can compute rigid transformation, correspondence, and overlapping percentage automatically at each iterative step. This new algorithm uses as many point pairs as possible to yield a more reliable and accurate registration result between two m-D point sets with outliers. Experimental results demonstrate that our algorithm is more robust than the traditional ICP and the state-of-the-art algorithms.
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