Building reconstruction from airborne laser scanning data

Huang, Xianfeng

doi:10.1080/10095020.2013.774104

Cited by 9 publications

(4 citation statements)

References 34 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model-driven methods [35][36][37][38][39], known as top-down or parametric methods, start from predefined parametric 3D roof structures and then fit a building model that is bestfitted the input point cloud, resulting in some simple roof blocks in the early stage. Kada and McKinley [40] decomposed the LiDAR point cloud data into multiple objects and then combined them into a whole model.…”

Section: Unsupervised Methodsmentioning

confidence: 99%

Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency

Miao

Hou

2021

Remote Sensing

View full text Add to dashboard Cite

Three-dimensional (3D) building models are closely related to human activities in urban environments. Due to the variations in building styles and complexity in roof structures, automatically reconstructing 3D buildings with semantics and topology information still faces big challenges. In this paper, we present an automated modeling approach that can semantically decompose and reconstruct the complex building light detection and ranging (LiDAR) point clouds into simple parametric structures, and each generated structure is an unambiguous roof semantic unit without overlapping planar primitive. The proposed method starts by extracting roof planes using a multi-label energy minimization solution, followed by constructing a roof connection graph associated with proximity, similarity, and consistency attributes. Furthermore, a progressive decomposition and reconstruction algorithm is introduced to generate explicit semantic subparts and hierarchical representation of an isolated building. The proposed approach is performed on two various datasets and compared with the state-of-the-art reconstruction techniques. The experimental modeling results, including the assessment using the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark LiDAR datasets, demonstrate that the proposed modeling method can efficiently decompose complex building models into interpretable semantic structures.

show abstract

Section: Unsupervised Methodsmentioning

confidence: 99%

Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency

Miao

Hou

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Model-driven approaches: it searches the most appropriate models that are fitted by the corresponding given point clouds among primitive building models contained in a predefined model library (Huang, 2013;Karantzalos and Paragios, 2010), but only relatively simple blocks were allowed to reconstruct in the early stages. The model-driven approaches are robust to reconstruct buildings because some of the previous knowledge like parallel and symmetry can be easily combined, and can create a watertight geometric model with semantic labelling.…”

Section: D Reconstructionmentioning

confidence: 99%

Reconstruction of 3d Models From Point Clouds With Hybrid Representation

Dong

Yuan

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

The three-dimensional (3D) reconstruction of urban buildings from point clouds has long been an active topic in applications related to human activities. However, due to the structures significantly differ in terms of complexity, the task of 3D reconstruction remains a challenging issue especially for the freeform surfaces. In this paper, we present a new reconstruction algorithm which allows the 3D-models of building as a combination of regular structures and irregular surfaces, where the regular structures are parameterized plane primitives and the irregular surfaces are expressed as meshes. The extraction of irregular surfaces starts with an over-segmented method for the unstructured point data, a region growing approach based the adjacent graph of super-voxels is then applied to collapse these super-voxels, and the freeform surfaces can be clustered from the voxels filtered by a thickness threshold. To achieve these regular planar primitives, the remaining voxels with a larger flatness will be further divided into multiscale super-voxels as basic units, and the final segmented planes are enriched and refined in a mutually reinforcing manner under the framework of a global energy optimization. We have implemented the proposed algorithms and mainly tested on two point clouds that differ in point density and urban characteristic, and experimental results on complex building structures illustrated the efficacy of the proposed framework.

show abstract

“…Image‐to‐lidar registration has become increasingly important for many applications, such as large scale texture mapping for 3D building models (Huang, ), image‐based upsampling of range data (Wang et al., 2012), change detection, cultural‐heritage preservation and environmental assessment. These applications, however, are not limited to urban areas.…”

Section: Introductionmentioning

confidence: 99%

Registration of Aerial Imagery and Lidar Data in Desert Areas Using Sand Ridges

Huang

Zhang

et al. 2015

The Photogrammetric Record

Self Cite

View full text Add to dashboard Cite

Image registration is a prerequisite for multisource data fusion. In this paper the problem of registering aerial images with lidar point clouds in desert areas is addressed. Compared with urban areas, registration in desert regions is difficult due to the lack of man-made features which are typically used in traditional methods. However, sand ridges can be used as registration primitives. Firstly, sand-ridge information is extracted from both the aerial image and the lidar point cloud. Secondly, by extending the iterative closest point (ICP) approach, a perspective-ICP algorithm is proposed that achieves data registration through matching sand ridges. To automatically deal with outliers, an adaptive weighting strategy is adopted. Experiments and assessment using data from Dunhuang, Gobi Desert, China, demonstrate that the method can achieve efficient and reliable registration for desert areas. Perspective-ICPThe perspective-ICP algorithm is used to register the two different data sets. Given the 2D aerial-image point q j in point set q and 3D lidar point P i in point set P, the registration must minimise the objective function:The Photogrammetric Record R esum e Le recalage d'images est un pr ealable a la fusion de donn ees multisource. Cet article aborde le probl eme du recalage entre des images a eriennes et des nuages de points lidar en r egion d esertique. Le recalage est plus difficile dans les d eserts qu'en milieu urbain en raison de la raret e des objets artificiels que les m ethodes The Photogrammetric Record

show abstract

Building reconstruction from airborne laser scanning data

Cited by 9 publications

References 34 publications

Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency

Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency

Reconstruction of 3d Models From Point Clouds With Hybrid Representation

Registration of Aerial Imagery and Lidar Data in Desert Areas Using Sand Ridges

Contact Info

Product

Resources

About