Development of a novel simplification mask for multi-shot optical scanners

Abzal, A.; Saadatseresht, M.; Varshosaz, Masood

doi:10.1016/j.isprsjprs.2018.05.010

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Wei et al [21] simplified point cloud based on curvature co-occurrence histograms. Abzal et al [22] developed a simplification mask for multi-shot optical scanners to reduce point during the data acquisition phase. Although the direct simplification method can reduce the calculation overhead, retaining features of what becomes a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

Liu

Fan

et al. 2019

IEEE Access

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3D point cloud simplification is an important pretreatment in surface reconstruction for sparing computer resources and improving reconstruction speed. However, existing methods often sacrifice the simplification precision to improve the simplification speed, or sacrifice the speed to improve precision. A proper balance between the simplification speed and the simplification accuracy is still a challenge. In this paper, we propose a new simplification method based on the importance of point. Named as detail feature points simplified algorithm (DFPSA), this algorithm has distinct processes to achieve improvements in three aspects. First, a rule of k neighborhood search is set to ensure the points found are the closest to the sample point. In this way, the accuracy of calculated normal vector of the point cloud is significantly improved, and the search speed is largely increased. Second, a formula that considers multiple characteristics for measuring the importance of point is proposed. Thereupon, the main detail features of the point cloud are preserved. Finally, an octree structure is employed to simplify the remaining points, through which holes in reconstructing point cloud are obviously reduced. The DFPSA is applied to four different data sets, and the corresponding results are compared with those of other five algorithms. The experimental results demonstrate that the DFPSA brings better simplification effects than existing counterparts, and the DFPSA not only can simplify point cloud but also has good effect in simplifying subject's narrow contours.INDEX TERMS 3D geometric point cloud simplification, detail feature point, k neighborhood search rule, the importance of point.

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Section: Introductionmentioning

confidence: 99%

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

Liu

Fan

et al. 2019

IEEE Access

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“…However, the conventional PS (Woodham, 1980) is based on the ideal Lambertian reflectance model and analyzes per-pixel illumination observation variations. As a result, real-world objects with complicated reflectance geometry cannot fulfill the ideal Lambertian reflectance model, resulting in an inaccurate 3D model with global shape deformation (Shi et al, 2018;Abzal et al, 2018). Several works have been conducted to deal with non-Lambertian surfaces in which non-Lambertian reflectances were treated as outliers, such as techniques based on the RANSAC schemes (Mukaigawa et al, 2007), rank minimization (Wu et al, 2010), or median (Miyazaki et al, 2010).…”

Section: State Of the Artmentioning

confidence: 99%

FFT-Based Filtering Approach to Fuse Photogrammetry and Photometric Stereo 3d Data

Karami

Varshosaz

Menna

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Image-based 3D reconstruction has been successfully employed for micro-measurements and industrial quality control purposes. However, obtaining a highly-detailed and reliable 3D reconstruction and inspection of non-collaborative surfaces is still an open issue. Photometric stereo (PS) offers the high spatial frequencies of the surface, but the low frequency is erroneous due to the mathematical model's assumptions and simplifications on how light interacts with the object surface. Photogrammetry, on the other hand, gives precise low-frequency information but fails to utilize high frequencies. As a result, in this research, we present a fusion strategy in Fourier domain to replace the low spatial frequencies of PS with the corresponding photogrammetric frequencies in order to have correct low frequencies while maintaining high frequencies from PS. The proposed method was tested on three different objects. Different cloud-to-cloud comparisons were provided between reference data and the 3D points derived from the proposed method to evaluate high and low frequency information. The obtained 3D findings demonstrated how the proposed methodology generates a high-detail 3D reconstruction of the surface topography (below 20 µm) while maintaining low-frequency information (0.09 µm on average for three different testing objects) by fusing photogrammetric and PS depth data with the proposed FFT-based method.

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“…However, basic PS-based modeling solutions are mainly based on the Lambertian reflectance model (Woodham, 1980;Shi et al, 2013Shi et al, , 2018Li et al, 2020) to describe light behaviour. The performance of such techniques degrades on real-world objects with properties such as interreflection and specular reflection (Abzal et al, 2018), which frequently exhibit non-Lambertian reflectance (Goldman et al, 2009;Shi et al, 2013Shi et al, , 2018Park et al, 2016;Li et al, 2020). As a result, instead of assuming the Lambertian reflectance model, some methods use a more complex and/or physical light model when interacting with the surface (Alldrin et al, 2008;Xie et al, 2010;Lu et al, 2013;Zheng et al, 2019;Boss et al, 2020;Santo et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Exploiting Light Directionality for Image‐Based 3D Reconstruction of Non‐Collaborative Surfaces

Karami

Menna

Remondino

et al. 2022

The Photogrammetric Record

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Three-dimensional (3D) measurement of non-collaborative surfaces is still an open research topic. This paper investigates and quantifies for the first time the effect of light directionality and fusion of multiple images as a method to improve the quality of photogrammetric 3D reconstruction. For this aim, an image acquisition system that employs multiple light sources was developed to highlight the roughness and microstructures of the object under investigation. Images were captured at various grazing angles to highlight the local surface roughness and microstructures. Individual point clouds, created using images taken at different grazing angles, were produced using dense image-matching techniques. These point clouds were then compared against different 3D photogrammetric reconstructions obtained from a pre-processing of the acquired images based on diffuse lighting, median and average images. Experiments showed that exploiting light directionality significantly improves image-matching quality. Furthermore, depending on the light direction, the root mean square (RMS) error of the 3D surfaces obtained using the proposed system were up to 50% less than those created by traditional diffuse lighting.

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Development of a novel simplification mask for multi-shot optical scanners

Cited by 8 publications

References 22 publications

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

FFT-Based Filtering Approach to Fuse Photogrammetry and Photometric Stereo 3d Data

Exploiting Light Directionality for Image‐Based 3D Reconstruction of Non‐Collaborative Surfaces

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