<i>Metro</i>: Measuring Error on Simplified Surfaces

Cignoni, Paolo; Rocchini, Claudio; Scopigno, Roberto

doi:10.1111/1467-8659.00236

Cited by 1,251 publications

(740 citation statements)

References 14 publications

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“…These methods have been implemented in C++. Moreover, Poisson surface reconstruction method, some metrics like the Hausdorff distance [22] and the visualization framework have been implemented using the PCL library [23] and the Meshlab tool.…”

Section: Methodsmentioning

confidence: 99%

“…This figure shows the results of applying both reconstruction methods to noisy synthetic point clouds with different Gaussian error. In order to evaluate these reconstructions in a quantitative way, we computed Hausdorff distance (using the Metro tool [22]) between the synthetic mesh model and the reconstructed ones from the noisy data. In this way, we can measure the quality of the reconstruction obtained with the different methods using noisy data.…”

Section: Surface Reconstruction Qualitymentioning

confidence: 99%

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3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

Orts-Escolano

García-Rodríguez

Morell

et al. 2015

Neural Process Lett

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With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the Growing Neural Gas (GNG) network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on Self-Organizing Maps (SOMs) required postprocessing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.

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Section: Methodsmentioning

confidence: 99%

Section: Surface Reconstruction Qualitymentioning

confidence: 99%

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

Orts-Escolano

García-Rodríguez

Morell

et al. 2015

Neural Process Lett

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show abstract

“…It creates mesh or triangulated models of the reference point cloud, which are used to measure the orthogonal distances for each point in the compared cloud (Cignoni and Rocchini 1998, see also Monserrat andCrosetto 2008 andOlsen et al 2010 for recent reviews). This procedure is most suited with sub-planar objects, due to a tendency to smooth out details possibly relevant in local properties evaluation.…”

Section: Point Clouds Comparison Algorithmsmentioning

confidence: 99%

Structure from motion (SfM) processing of UAV images and combination with terrestrial laser scanning, applied for a 3D-documentation in a hazardous situation

Zaragoza

Crepaldi

Piemonte

et al. 2017

Geomatics, Natural Hazards and Risk

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The integration of Terrestrial Laser Scanning (TLS) and Structure from Motion and MultiView Stereo techniques allows to obtain comprehensive models of complex objects by using each technique in contexts presenting the optimal operating conditions, as widely reported in bibliographic references. A different situation occurs for emergency surveys. In this case, time and security act as constraining factors, requiring the use of these techniques also in the most unfavourable conditions. In the case of photogrammetry, these include areas where the object surfaces are not perpendicular to the camera axis, and in the case of TLS, they include areas where laser beams are almost tangent to the surveyed object surfaces. These situations are anyway necessary for safely carrying out these surveys in the minimum possible time and cost. Although this kind of survey results locally in lower precision levels than those obtainable by these techniques in ideal conditions, it entails the possibility of obtaining complete models, e.g. including vertical external walls in inaccessible buildings, with controlled precision.

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“…Furthermore, operations and analyses may introduce additional error. Our model of scientific data includes localized error that is estimated at every point within the domain [3,19].…”

Section: Errormentioning

confidence: 99%

A Data Model for Distributed Multiresolution Multisource Scientific Data

Rhodes

Sparr

2003

Mathematics and Visualization

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Summary.Modern dataset sizes present major obstacles to understanding and interpreting the significant underlying phenomena represented in the data. There is a critical need to support scientists in the process of interactive exploration of these very large data sets. Using multiple resolutions of the data set (multiresolution), the scientist can identify potentially interesting regions with a coarse overview, followed by narrower views at higher resolutions.Scientific data sets are often multisourcecoming from different sources. Although it may be infeasible to physically combine multiple datasets into a single comprehensive dataset, the scientist would often like to treat them as a single logical entity. This paper describes formal conceptual models of multiresolution and distributed multisource scientific data along with an implementation of our multisource model. Our goal is to allow a scientist to describe a dataset that combines several multisource multiresolution datasets into a single conceptual entity and to provide efficient and transparent access to the data based on functionality defined by the model.

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Metro: Measuring Error on Simplified Surfaces

Cited by 1,251 publications

References 14 publications

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

Structure from motion (SfM) processing of UAV images and combination with terrestrial laser scanning, applied for a 3D-documentation in a hazardous situation

A Data Model for Distributed Multiresolution Multisource Scientific Data

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