Quality Features for the Integration of Terrestrial and Uav Images

Farella, Elisa Mariarosaria; Torresani, Alessandro; Remondino, Fabio

doi:10.5194/isprs-archives-xlii-2-w9-339-2019

Cited by 15 publications

(14 citation statements)

References 23 publications

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“…The presented procedure will also be extended, including new methods for automatically defining eigen-filtering thresholds and for estimating the improvements of other inner quality parameters after the filtering step (such as the variation of the re-projection error or the multiplicity values). Finally, the developed methodology will be combined with the procedure presented in Farella et al (2019), considering in the filtering step photogrammetric acquisition issues and reconstructed geometric properties of the points.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous work (Farella et al, 2019), we implemented a filtering procedure on the sparse point cloud to remove outliers and bad computed points and improve the bundle adjustment results before performing the final dense reconstruction. This procedure focused on removing bad 3D tie points based on some quality features, mainly indicative of a wrong acquisition procedure and some photogrammetric reconstruction issues.…”

Section: Aim Of the Papermentioning

confidence: 99%

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Sparse Point Cloud Filtering Based on Covariance Features

Farella

Torresani

Remondino

2019

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

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Abstract. This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Aim Of the Papermentioning

confidence: 99%

Sparse Point Cloud Filtering Based on Covariance Features

Farella

Torresani

Remondino

2019

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

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“…Numerous examples exist in the literature on the use of 3D techniques for heritage documentation, e.g., the work of [17][18][19] to cite a few. Another trend that has surfaced as a logical consequence of the availability of multiple sensors is data integration, both in the sensor level and the point cloud level [20][21][22][23].…”

Section: General State-of-the-artmentioning

confidence: 99%

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Murtiyoso

Grussenmeyer

2020

Sensors

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3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms.

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“…A SIFT-like operator is adopted to automatically extract a large number of tie points (more than 5 million points) that are then exported in the form of both image observations and corresponding 3D coordinates. These data are later filtered and regularized by applying an inhouse developed tool, based on a weighted combination of three criteria: re-projection error, intersection angle of image rays and their multiplicity (Farella et al, 2019). The filtered and more reliable correspondences (almost 2 million points) are subsequently imported as image observations into the bundle adjustment, where a free-network self-calibrating adjustment is again performed.…”

Section: Image Orientationmentioning

confidence: 99%

A Multi-Purpose Benchmark for Photogrammetric Urban 3d Reconstruction in a Controlled Environment

Özdemir

Toschi

Remondino

2019

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

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Abstract. This paper presents a novel multi-purpose benchmark for assessing the performance of the entire image-based pipeline for 3D urban reconstruction and 3D data classification. The proposed benchmark is called “3DOMcity” photogrammetric contest and is publicly available at https://3dom.fbk.eu/3domcity-benchmark. Compared to previous benchmark initiatives, the innovative aspects introduced by 3DOMcity are threefold. First, it covers the entire 3D reconstruction pipeline, including image orientation, dense image matching, point cloud classification and 3D building reconstruction. Second, the performance assessment is performed within a metrological context. Finally, the provided datasets offer 2D and 3D data collected at a very high spatial resolution. Furthermore, the benchmark is multi-purpose since it involves a multiplicity of tasks, that can be either performed independently from each other’s, or grouped together. The contributions of this paper are (i) the presentation of the novel benchmark dataset and corresponding reference data, (ii) the description of the multi-purpose benchmark tasks and evaluation procedures, and (iii) the discussion of first results of image orientation, dense image matching and classification.

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Quality Features for the Integration of Terrestrial and Uav Images

Cited by 15 publications

References 23 publications

Sparse Point Cloud Filtering Based on Covariance Features

Sparse Point Cloud Filtering Based on Covariance Features

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

A Multi-Purpose Benchmark for Photogrammetric Urban 3d Reconstruction in a Controlled Environment

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