The performance evaluation of multi-image 3D reconstruction software with different sensors

Mousavi, Vahid; Khosravi, Mohammad A.; Ahmadi, Mohammad Javad; Noori, Nadia Saad; Haghshenas, Sh.; Ahmadabadian, Ali Hosseininaveh; Varshosaz, Masood

doi:10.1016/j.measurement.2018.01.058

Cited by 26 publications

(23 citation statements)

References 27 publications

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“…Over the years, different photogrammetric methods have been developed to deal with the 3D reconstruction of noncollaborative objects. For 3D reconstruction of Lambertian textureless objects, previous studies have been concentrated on improving surface texture by projecting, for example, a known pattern (Menna et al, 2017;Mousavi et al, 2018), random (Hosseininaveh et al, 2015;Ahmadabadian et al, 2019) or a synthetic texture (Santoši et al, 2019;Hafeez et al, 2020) onto the objects. These methods, however, assume that the object surface is Lambertian, which is not the case with objects that have specular reflection or interreflection effects.…”

Section: Photogrammetrymentioning

confidence: 99%

Investigating 3d Reconstruction of Non-Collaborative Surfaces Through Photogrammetry and Photometric Stereo

Karami

Menna

Remondino

2021

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

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Abstract. 3D digital reconstruction techniques are extensively used for quality control purposes. Among them, photogrammetry and photometric stereo methods have been for a long time used with success in several application fields. However, generating highly-detailed and reliable micro-measurements of non-collaborative surfaces is still an open issue. In these cases, photogrammetry can provide accurate low-frequency 3D information, whereas it struggles to extract reliable high-frequency details. Conversely, photometric stereo can recover a very detailed surface topography, although global surface deformation is often present. In this paper, we present the preliminary results of an ongoing project aiming to combine photogrammetry and photometric stereo in a synergetic fusion of the two techniques. Particularly, hereafter, we introduce the main concept design behind an image acquisition system we developed to capture images from different positions and under different lighting conditions as required by photogrammetry and photometric stereo techniques. We show the benefit of such a combination through some experimental tests. The experiments showed that the proposed method recovers the surface topography at the same high-resolution achievable with photometric stereo while preserving the photogrammetric accuracy. Furthermore, we exploit light directionality and multiple light sources to improve the quality of dense image matching in poorly textured surfaces.

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Section: Photogrammetrymentioning

confidence: 99%

Investigating 3d Reconstruction of Non-Collaborative Surfaces Through Photogrammetry and Photometric Stereo

Karami

Menna

Remondino

2021

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

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“…Due to a large number of keypoint detectors and descriptors, many surveys [10,11] discussing their advantages and drawbacks have been reported. Furthermore, many works have evaluated and compared the performance of keypoint extraction and description techniques for various image matching tasks [12][13][14][15]. Clearly, the number and distribution of the extracted keypoints play a vital role in the reliability and accuracy of the image matching results, which is a very critical factor in high-resolution UAV photogrammetry bundle adjustment [16].…”

Section: Introductionmentioning

confidence: 99%

Using Information Content to Select Keypoints for UAV Image Matching

Mousavi¹,

Varshosaz²,

Remondino³

2021

Remote Sensing

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Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.

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“…These developments have enabled more automation, higher velocities, increased accuracy, and precision. Especially, improvements in new instruments and digital tools, such as handheld scanners and photogrammetric automated or semi-automated software, provide powerful 3D digitization solutions for the experts and inexperienced users both [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Critical Comparison of 3D Digitization Techniques for Heritage Objects

Adamopoulos

Rinaudo

Ardissono

2020

IJGI

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Techniques for the three-dimensional digitization of tangible heritage are continuously updated, as regards active and passive sensors, data acquisition approaches, implemented algorithms and employed computational systems. These developments enable higher automation and processing velocities, increased accuracy, and precision for digitizing heritage assets. For large-scale applications, as for investigations on ancient remains, heritage objects, or architectural details, scanning and image-based modeling approaches have prevailed, due to reduced costs and processing durations, fast acquisition, and the reproducibility of workflows. This paper presents an updated metric comparison of common heritage digitization approaches, providing a thorough examination of sensors, capturing workflows, processing parameters involved, metric and radiometric results produced. A variety of photogrammetric software were evaluated (both commercial and open sourced), as well as photo-capturing equipment of various characteristics and prices, and scanners employing different technologies. The experimentations were performed on case studies of different geometrical and surface characteristics to thoroughly assess the implemented three-dimensional modeling pipelines.

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The performance evaluation of multi-image 3D reconstruction software with different sensors

Cited by 26 publications

References 27 publications

Investigating 3d Reconstruction of Non-Collaborative Surfaces Through Photogrammetry and Photometric Stereo

Investigating 3d Reconstruction of Non-Collaborative Surfaces Through Photogrammetry and Photometric Stereo

Using Information Content to Select Keypoints for UAV Image Matching

A Critical Comparison of 3D Digitization Techniques for Heritage Objects

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