A Review of Existing Evaluation Methods for Point Clouds Quality

Fretes, Héctor; Gomez-Redondo, Marcos; Paiva, Enrique; Rodas, Jorge; Gregor, Raúl

doi:10.1109/reduas47371.2019.8999725

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Each point in the sparse cloud has four accuracy attributes: the reconstruction accuracy (RA), the reprojection error (RE), the projection accuracy (PA) and the image count [35]. In particular, the RE is suggested as the quality measure of tie points [20,36]. It is the deviation of the positions of identified features in the original image from positions of the same features in the calculated 3D space.…”

Section: Optimizing the Georeferencingmentioning

confidence: 99%

Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

et al. 2020

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Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.

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Section: Optimizing the Georeferencingmentioning

confidence: 99%

Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

et al. 2020

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“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ essential to develop effective methods that accurately assess the quality of PC and preserve the quality of the user experience. Two families of methods are usually adopted to evaluate PC degradation [11], [12], [13]: subjective and objective metrics. The subjective ones are based on human judgment for the evaluation, which makes it cumbersome and expansive in practical real-world situations.…”

Section: Introductionmentioning

confidence: 99%

No-Reference 3D Point Cloud Quality Assessment Using Multi-View Projection and Deep Convolutional Neural Network

et al. 2023

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Digital representation of 3D content in the form of 3D point clouds (PC) has gained increasing interest and has emerged in various computer vision applications. However, various degradation may appear on the PC during acquisition, transmission, or treatment steps in the 3D processing pipeline. Therefore, several Full-Reference, Reduced-Reference, and No-Reference metrics have been proposed to estimate the visual quality of PC. However, Full-Reference and Reduced-Reference metrics require reference information, which is not accessible in real-world applications, and No-Reference metrics still lack precision in evaluating the PC quality. In this context, we propose a novel deep learning-based method for No-Reference Point Cloud Quality Assessment (NR-PCQA) that aims to automatically predict the perceived visual quality of the PC without using the reference content. More specifically, in order to imitate the human visual system during the PC quality evaluation that captures the geometric and color degradation, we render the PC into different 2D views using a perspective projection. Then, the projected 2D views are divided into patches that are fed to a Convolutional Neural Network (CNN) to learn sophisticated and discriminative visual quality features for evaluating the local quality of each patch. Finally, the overall quality score of the PC is obtained by pooling the quality score patches. We conduct extensive experiments on three benchmark databases: ICIP2020, SJTU, and WPC, and we compare the proposed model to the existing Full-Reference, Reduced-Reference, and No-Reference state-of-the-art methods. Based on the experimental results, our proposed model achieves high correlations with the subjective quality scores and outperforms the state-of-the-art methods.INDEX TERMS Point cloud, quality assessment, point cloud rendering, convolutional neural network (CNN).

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Super-Resolution Techniques in Photogrammetric 3D Reconstruction from Close-Range UAV Imagery

et al. 2023

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Current Multi-View Stereo (MVS) algorithms are tools for high-quality 3D model reconstruction, strongly depending on image spatial resolution. In this context, the combination of image Super-Resolution (SR) with image-based 3D reconstruction is turning into an interesting research topic in photogrammetry, around which however only a few works have been reported so far in the literature. Here, a thorough study is carried out on various state-of-the-art image SR techniques to evaluate the suitability of such an approach in terms of its inclusion in the 3D reconstruction process. Deep-learning techniques are tested here on a UAV image dataset, while the MVS task is then performed via the Agisoft Metashape photogrammetric tool. The data under experimentation are oblique cultural heritage imagery. According to results, point clouds from low-resolution images present quality inferior to those from upsampled high-resolution ones. The SR techniques HAT and DRLN outperform bicubic interpolation, yielding high precision/recall scores for the differences of reconstructed 3D point clouds from the reference surface. The current study indicates spatial image resolution increased by SR techniques may indeed be advantageous for state-of-the art photogrammetric 3D reconstruction.

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A Review of Existing Evaluation Methods for Point Clouds Quality

Cited by 3 publications

References 34 publications

Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

No-Reference 3D Point Cloud Quality Assessment Using Multi-View Projection and Deep Convolutional Neural Network

Super-Resolution Techniques in Photogrammetric 3D Reconstruction from Close-Range UAV Imagery

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