Structure‐Aware Mesh Decimation

Salinas, David; Lafarge, Florent; Alliez, Pierre

doi:10.1111/cgf.12531

Cited by 58 publications

(69 citation statements)

References 25 publications

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“…[14] at the pixel scale gives dense and structure-free 3D models. By postprocessing a DSM with Voronoi clustering [27] or with structure-aware mesh simplification [33], we obtain more compact meshes, but the building structure cannot be not restored. Our output model is both compact and structure-aware (see the low number of principal directions in the distribution of output normals).…”

Section: Methodsmentioning

confidence: 99%

Towards Large-Scale City Reconstruction from Satellites

Duan

Lafarge

2016

Lecture Notes in Computer Science

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Abstract. Automatic city modeling from satellite imagery is one of the biggest challenges in urban reconstruction. Existing methods produce at best rough and dense Digital Surface Models. Inspired by recent works on semantic 3D reconstruction and region-based stereovision, we propose a method for producing compact, semantic-aware and geometrically accurate 3D city models from stereo pair of satellite images. Our approach relies on two key ingredients. First, geometry and semantics are retrieved simultaneously bringing robustness to occlusions and to low image quality. Second, we operate at the scale of geometric atomic region which allows the shape of urban objects to be well preserved, and a gain in scalability and efficiency. We demonstrate the potential of our algorithm by reconstructing different cities around the world in a few minutes.

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Section: Methodsmentioning

confidence: 99%

Towards Large-Scale City Reconstruction from Satellites

Duan

Lafarge

2016

Lecture Notes in Computer Science

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“…However, we can see that over sloped roofs and within a larger block of buildings, the surface orientations vary too much, allowing the algorithm to produce good results on only one of the three inputs. Finally, we compare our method to structure-aware mesh decimation [Salinas et al 2015], which also produces good results, but only a part of the model is simplified.…”

Section: Comparisonmentioning

confidence: 99%

“…Another observed failure case occurs when roof gutters do not align to detected building-façade boundaries, as our optimization assumes such situations are noisy data. Finally, our core [Kazhdan et al 2006], Manhattan box fitting [Li et al 2016], Structure-Aware Mesh Decimation [Salinas et al 2015] and our technique (without GIS footprints or building-façade inputs).…”

Section: Limitationsmentioning

confidence: 99%

BigSUR

et al. 2017

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Fig. 1. Structured Urban Reconstruction. Given street-level imagery, GIS footprints, and a coarse 3D mesh (left), we formulate a global optimization to automatically fuse these noisy, incomplete, and conflicting data sources to create building footprints (middle: colored horizontal polygons) with profiles (vertical ribbons shown for several footprints) and attached building façades (vertical rectangles). The output encodes a structured urban model (right) including the walls, roof, and associated building elements (e.g., windows, balconies, roof, wall color, etc.). Inset below: A reference aerial image.The creation of high-quality semantically parsed 3D models for dense metropolitan areas is a fundamental urban modeling problem. Although recent advances in acquisition techniques and processing algorithms have resulted in large-scale imagery or 3D polygonal reconstructions, such data-sources are typically noisy, and incomplete, with no semantic structure. In this paper, we present an automatic data fusion technique that produces high-quality structured models of city blocks. From coarse polygonal meshes, street-level imagery, and GIS footprints, we formulate a binary integer program that globally balances sources of error to produce semantically parsed mass models with associated façade elements. We demonstrate our system on four city regions of varying complexity; our examples typically contain densely built urban blocks spanning hundreds of buildings. In our largest example, we produce a structured model of 37 city blocks spanning a total of 1,011 buildings at a scale and quality previously impossible to achieve automatically.

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“…And a min-cut formulation applied to a discrete approximation of a 3D planar arrangement for robust reconstruction. Salinas et al (2015) proposed a structure-aware mesh decimation method that mainly used to simplify dense surface meshes of complex buildings in urban scene. This approach takes as input a surface triangle mesh and a set of planar proxies pre-detected, and generates as output a simplified mesh where coarse-scale structures are preserved via an error metric and specific rules.…”

Section: Building Reconstruction From Oblique Imagerymentioning

confidence: 99%

Feature Line Based Building Detection and Reconstruction From Oblique Airborne Imagery

Zhu

Jiang

Zhang

2015

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

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ABSTRACT:In this paper, a feature line based method for building detection and reconstruction from oblique airborne imagery is presented. With the development of Multi-View Stereo technology, increasing photogrammetric softwares are provided to generate textured meshes from oblique airborne imagery. However, errors in image matching and mesh segmentation lead to the low geometrical accuracy of building models, especially at building boundaries. To simplify massive meshes and construct accurate 3D building models, we integrate multi-view images and meshes by using feature lines, in which contour lines are used for building detection and straight skeleton for building reconstruction. Firstly, through the contour clustering method, buildings can be quickly and robustly detected from meshes. Then, a feature preserving mesh segmentation method is applied to accurately extract 3D straight skeleton from meshes. Finally, straight feature lines derived from multi-view images are used to rectify inaccurate parts of 3D straight skeleton of buildings. Therefore, low quality model can be refined by the accuracy improvement of mesh feature lines and rectification with feature lines of multi-view images. The test dataset in Zürich is provided by ISPRS/EuroSDR initiative Benchmark on High Density Image Matching for DSM Computation. The experiments reveal that the proposed method can obtain convincing and high quality 3D building models from oblique airborne imagery.

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Structure‐Aware Mesh Decimation

Cited by 58 publications

References 25 publications

Towards Large-Scale City Reconstruction from Satellites

Towards Large-Scale City Reconstruction from Satellites

BigSUR

Feature Line Based Building Detection and Reconstruction From Oblique Airborne Imagery

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