Shoujun Jia, Chun Liu, Hangbin Wu, Weihua Huan, Shufan Wang
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引用次数: 0
Abstract
The increasing availability of point cloud acquisition techniques makes it possible to significantly increase 3D observation capacity by the registration of multi-sensor, multi-platform, and multi-temporal point clouds. However, there are geometric heterogeneities (point density variations and point distribution differences), small overlaps (30 % ∼ 50 %), and large data amounts (a few millions) among these large-scale heterogeneous point clouds, which pose great challenges for effective and efficient registration. In this paper, considering the structural representation capacity of graph model, we propose an incremental registration method for large-scale heterogeneous point clouds by hierarchical graph matching. More specifically, we first construct a novel graph model to discriminatively and robustly represent heterogeneous point clouds. In addition to conventional nodes and edges, our graph model particularly designs discriminative and robust feature descriptors for local node description and captures spatial relationships from both locations and orientations for global edge description. We further devise a matching strategy to accurately estimate node matches for our graph models with partial even small overlaps. This effectiveness benefits from the comprehensiveness of node and edge dissimilarities and the constraint of geometric consistency in the optimization objective. On this basis, we design a coarse-to-fine registration framework for effective and efficient point cloud registration. In this incremental framework, graph matching is hierarchically utilized to achieve sparse-to-dense point matching by global extraction and local propagation, which provides dense correspondences for robust coarse registration and predicts overlap ratio for accurate fine registration, and also avoids huge computation costs for large-scale point clouds. Extensive experiments on one benchmark and three changing self-built datasets with large scales, outliers, changing densities, and small overlaps show the excellent transformation and correspondence accuracies of our registration method for large-scale heterogeneous point clouds. Compared to the state-of-the-art methods (i.e., TrimICP, CoBigICP, GROR, VPFBR, DPCR, and PRR), our registration method performs approximate even higher efficiency while achieves an improvement of 33 % − 88 % regarding registration accuracy (OE).
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.