Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds

Gao, Xiang; Shen, Shuhan; Zhou, Yang; Cui, Hainan; Zhu, Longxiang; Hu, Zhanyi

doi:10.1016/j.isprsjprs.2018.04.023

Cited by 34 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Manhattan-Modeler retrieves the façade using the height map, which can only be used to repair vertical walls [39]. Other methods involve ground data aimed at façade modeling, which introduces extra data cost [40][41][42][43].…”

Section: Topology Repair Towards the Incomplete Datasetmentioning

confidence: 99%

TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

et al. 2019

View full text Add to dashboard Cite

Limited by the noise, missing data and varying sampling density of the point clouds, planar primitives are prone to be lost during plane segmentation, leading to topology errors when reconstructing complex building models. In this paper, a pipeline to recover the broken topology of planar primitives (TopoLAP) is proposed to reconstruct level of details 3 (LoD3) models. Firstly, planar primitives are segmented from the incomplete point clouds and feature lines are detected both from point clouds and images. Secondly, the structural contours of each plane segment are reconstructed by subset selection from intersections of these feature lines. Subsequently, missing planes are recovered by plane deduction according to the relationships between linear and planar primitives. Finally, the manifold and watertight polyhedral building models are reconstructed based on the optimized PolyFit framework. Experimental results demonstrate that the proposed pipeline can handle partial incomplete point clouds and reconstruct the LoD3 models of complex buildings automatically. A comparative analysis indicates that the proposed method performs better to preserve sharp edges and achieves a higher fitness and correction rate than rooftop-based modeling and the original PolyFit algorithm.

show abstract

Section: Topology Repair Towards the Incomplete Datasetmentioning

confidence: 99%

TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

et al. 2019

View full text Add to dashboard Cite

show abstract

“…HREE dimensional (3D) building models with detailed roofs are essential for a large number of geospatial applications, such as 3D Geographic Information Systems (GIS), urban planning, environmental simulation, energy consumption assessment, heritage preservation [1], and change detection [2]. With the rapid developments in aerial laser scanning and aerial oblique photogrammetry, building reconstruction from point clouds has been an active and ongoing research topic in photogrammetry and compute vision.…”

Section: Introductionmentioning

confidence: 99%

Optimal Model Fitting for Building Reconstruction From Point Clouds

Zhang¹,

Li²,

Shan³

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Geometric-semantic coherent building models are demanding in many geoscience applications. Conventional building modeling methods often rely on successive roof plane segmentation and fitting. The subsequent reconstruction procedure is difficult to assure topologic consistency and geometric accuracy. This paper starts with a library of predefined building models or primitives, including pyramid, gable, hip, etc. We propose an optimal model fitting approach that holistically determines all of its parameters from segmented point cloud data. The approach is formulated as an optimization problem that minimizes the point-to-mesh distance between the point cloud and the meshed primitive model. Necessary constraints in the form of inequality equations are introduced to assure correct and reliable solution. For complex roofs consisting of several predefined primitive models, a hierarchical procedure is presented to reconstruct the major roof model and its superstructures sequentially. The CityGML LoD2 model is created from the parameterized primitives. The quality and performance of this approach are evaluated with airborne lidar and photogrammetric point clouds. Based on the experiments with 910 buildings, the primitive fitting accuracy is 7.8cm and the corner uncertainty is 0.36m or 0.78 times the ground point spacing; the building boundary consistency is 89.6%. The study demonstrates a piecewise continuous polyhedral building model can be determined through a holistic parameter optimization process. The resultant building models intrinsically best fit to the input point cloud with topologic integrity. The approach not only qualitatively generates semantic building models, but also exhibits the potential for building reconstruction over large areas.

show abstract

“…While aerial view images provide a larger and more complete range of top views, street view images delineate points of interest and details of urban façades. Establishing correspondence between street (ground) and aerial view images can facilitate a variety of urban applications that require supplementary information from both views, ranging from geolocating [1], [2], urban 3D modeling [3], [4], [5], and autonomous driving [6] to ancient architecture preservation [7]. However, establishing such correspondence is a particularly challenging problem due to the drastic changes in viewpoint, scale, and illumination between ground and aerial images.…”

Section: Introductionmentioning

confidence: 99%

Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images

Zheng

Xiong

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true-false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this paper. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true-false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods.

show abstract

Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds

Cited by 34 publications

References 25 publications

TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

Optimal Model Fitting for Building Reconstruction From Point Clouds

Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images

Contact Info

Product

Resources

About