Surface Reconstruction from 3D Line Segments

Langlois, Pierre-Alain; Boulch, Alexandre; Marlet, Renaud

doi:10.1109/3dv.2019.00067

Cited by 14 publications

(12 citation statements)

References 62 publications

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“…Screened Poisson reconstruction [KH13] is applied to reconstruct surfaces from both the sparse (Figure 13 Compare to methods merely using point cloud, line-based methods are more robust in the plane detection (Figure 13(f-g)). However, Langlois et al [LBM19] don't take plane's boundary into consideration. These planes partition space into massive cells, which not only takes a long time to process (usually takes over 30min) but also produces artifact cells easily .…”

Section: Experiments and Discussionmentioning

confidence: 99%

Manhattan‐world urban building reconstruction by fitting cubes

Wang

Cheng

2021

Computer Graphics Forum

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The Manhattan‐world building is a kind of dominant scene in urban areas. Many existing methods for reconstructing such scenes are either vulnerable to noisy and incomplete data or suffer from high computational complexity. In this paper, we present a novel approach to quickly reconstruct lightweight Manhattan‐world urban building models from images. Our key idea is to reconstruct buildings through the salient feature ‐ corners. Given a set of urban building images, Structure‐from‐Motion and 3D line reconstruction operations are applied first to recover camera poses, sparse point clouds, and line clouds. Then we use orthogonal planes detected from the line cloud to generate corners, which indicate a part of possible buildings. Starting from the corners, we fit cubes to point clouds by optimizing corner parameters and obtain cube representations of corresponding buildings. Finally, a registration step is performed on cube representations to generate more accurate models. Experiment results show that our approach can handle some nasty cases containing noisy and incomplete data, meanwhile, output lightweight polygonal building models with a low time‐consuming.

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Section: Experiments and Discussionmentioning

confidence: 99%

Manhattan‐world urban building reconstruction by fitting cubes

Wang

Cheng

2021

Computer Graphics Forum

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“…As we will show in Section 3, when the data points are sparse, minimizing the area alone leads to undesirable "short-cutting" artifacts. Boulch and coworkers [Boulch et al 2014;Langlois et al 2019] introduced additional regularization terms that minimize the length of edges and number of corners on the reconstructed surface. While these terms reduce short-cuts, they cannot eliminate them (see Figure 5).…”

Section: Piecewise Planar Reconstructionmentioning

confidence: 99%

Boundary-sampled halfspaces

Zhou

Carr

et al. 2021

ACM Trans. Graph.

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We present a novel representation of solid models for shape design. Like Constructive Solid Geometry (CSG), the solid shape is constructed from a set of halfspaces without the need for an explicit boundary structure. Instead of using Boolean expressions as in CSG, the shape is defined by sparsely placed samples on the boundary of each halfspace. This representation, called Boundary-Sampled Halfspaces (BSH), affords greater agility and expressiveness than CSG while simplifying the reverse engineering process. We discuss theoretical properties of the representation and present practical algorithms for boundary extraction and conversion from other representations. Our algorithms are demonstrated on both 2D and 3D examples.

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“…Traditional photogrammetric techniques used vector constraints, the so-called breaklines, to tackle such depth discontinuities and imply geometric constraints during DSM generation (Briese, 2004). Similar to corner points, edges have been used to support various photogrammetric and computer vision tasks such as image matching (Wang et al, 2009;Wang et al, 2021), camera localization (Hirose and Saito, 2012;Salaün et al, 2017;Miraldo et al, 2018), abstract 3D scene representation (Hofer et al, 2015;, meshing sparse clouds (Bódis-Szomorú et al, 2015;Sugiura et al, 2015) as well as modelling and simplifying the scene (Langlois et al, 2019;Chen et al, 2020;Li and Nan, 2021).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Geometric Edge Details in MVS Reconstruction

Stathopoulou

Rigon

Battisti

et al. 2021

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

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Abstract. Mesh models generated by multi view stereo (MVS) algorithms often fail to represent in an adequate manner the sharp, natural edge details of the scene. The harsh depth discontinuities of edge regions are eventually a challenging task for dense reconstruction, while vertex displacement during mesh refinement frequently leads to smoothed edges that do not coincide with the fine details of the scene. Meanwhile, 3D edges have been used for scene representation, particularly man-made built environments, which are dominated by regular planar and linear structures. Indeed, 3D edge detection and matching are commonly exploited either to constrain camera pose estimation, or to generate an abstract representation of the most salient parts of the scene, and even to support mesh reconstruction. In this work, we attempt to jointly use 3D edge extraction and MVS mesh generation to promote edge detail preservation in the final result. Salient 3D edges of the scene are reconstructed with state-of-the-art algorithms and integrated in the dense point cloud to be further used in order to support the mesh triangulation step. Experimental results on benchmark dataset sequences using metric and appearance-based measures are performed in order to evaluate our hypothesis.

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Surface Reconstruction from 3D Line Segments

Cited by 14 publications

References 62 publications

Manhattan‐world urban building reconstruction by fitting cubes

Manhattan‐world urban building reconstruction by fitting cubes

Boundary-sampled halfspaces

Enhancing Geometric Edge Details in MVS Reconstruction

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