City Reconstruction From Airborne Lidar: A Computational Geometry Approach

Bauchet, Jean-Philippe; Lafarge, Florent

doi:10.5194/isprs-annals-iv-4-w8-19-2019

Cited by 15 publications

(11 citation statements)

References 27 publications

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“…These methods can thus hardly be applied to large-scale urban scenes. To facilitate practical applications that require large-scale 3D building models, researchers have attempted to address the reconstruction challenge using various data sources [9][10][11][12][13][14][15][16]. Existing methods based on aerial images [10,12,13] and dense triangle meshes [11] typically require good coverage of the buildings, which imposes challenges in data acquisition [17].…”

Section: Introductionmentioning

confidence: 99%

City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds

et al. 2022

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We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne LiDAR point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne LiDAR point clouds and the use of 3D city models in urban applications.

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Section: Introductionmentioning

confidence: 99%

City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds

et al. 2022

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“…Approaches based on airborne point clouds alleviate data acquisition issues. However, the accuracy and geometric details are usually compromised [4,22,25,49]. Following previous works using widely available airborne point clouds, we strive to recover desired geometric details of real-world buildings while ensuring topological correctness, reconstruction accuracy, and good efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can thus hardly be applied to large-scale urban scenes. To facilitate practical applications that require large-scale 3D building models, researchers have attempted to address the reconstruction challenge using various data sources [4,6,16,22,23,25,40,49]. Existing methods based on aerial images [6,16,23] and dense triangle meshes [40] typically require good coverage of the buildings, which imposes challenges in data acquisition [53].…”

Section: Introductionmentioning

confidence: 99%

City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds

Huang¹,

Stoter²,

Peters³

et al. 2022

Preprint

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We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne point clouds have demonstrated that the method is superior to the stateof-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne point clouds and the use of 3D city models in urban applications.

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“…In this type of approach, roof edges and vertices are mathematically represented by intersecting segmented planes. Recent research presented some plane-based methods that firstly segment the point clouds into primitive planes and then intersect them into polygonal meshes in 3D space (Bauchet and Lafarge 2019). Technically, such solutions are based on sequential local fitting, i.e., at one time only part of the entire data of a building participates in determining the building model.…”

Section: Introductionmentioning

confidence: 99%

Holistic Parametric Reconstruction of Building Models From Point Clouds

Li¹,

Zhang

Shan³

2020

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

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Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

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City Reconstruction From Airborne Lidar: A Computational Geometry Approach

Cited by 15 publications

References 27 publications

City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds

City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds

City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds

Holistic Parametric Reconstruction of Building Models From Point Clouds

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