FringeMatchNet: effective stereo matching onboard of mobile structured light 3D scanner

2019 ◽  
Author(s):  
Vladimir V. Kniaz
Keyword(s):  
Stereo Matching ◽  
Structured Light ◽  
3D Scanner

scholarly journals Structured-light 3D scanning of exhibited historical clothing—a first-ever methodical trial and its results

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jerzy Montusiewicz ◽  
Marek Miłosz ◽  
Jacek Kęsik ◽  
Kamil Żyła
Keyword(s):  
Cultural Heritage ◽  
Information Technologies ◽  
Structured Light ◽  
Three Dimensional ◽  
3D Models ◽  
3D Scanning ◽  
Dimensional Representation ◽  
3D Scanner ◽  
Three Dimensional Representation ◽  
3D Scanners

AbstractHistorical costumes are part of cultural heritage. Unlike architectural monuments, they are very fragile, which exacerbates the problems of their protection and popularisation. A big help in this can be the digitisation of their appearance, preferably using modern techniques of three-dimensional representation (3D). The article presents the results of the search for examples and methodologies of implementing 3D scanning of exhibited historical clothes as well as the attendant problems. From a review of scientific literature it turns out that so far practically no one in the world has made any methodical attempts at scanning historical clothes using structured-light 3D scanners (SLS) and developing an appropriate methodology. The vast majority of methods for creating 3D models of clothes used photogrammetry and 3D modelling software. Therefore, an innovative approach was proposed to the problem of creating 3D models of exhibited historical clothes through their digitalisation by means of a 3D scanner using structural light technology. A proposal for the methodology of this process and concrete examples of its implementation and results are presented. The problems related to the scanning of 3D historical clothes are also described, as well as a proposal how to solve them or minimise their impact. The implementation of the methodology is presented on the example of scanning elements of the Emir of Bukhara's costume (Uzbekistan) from the end of the nineteenth century, consisting of the gown, turban and shoes. Moreover, the way of using 3D models and information technologies to popularise cultural heritage in the space of digital resources is also discussed.

scholarly journals DENSE 3D OBJECT RECONSTRUCTION USING STRUCTURED-LIGHT SCANNER AND DEEP LEARNING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 777-783
Author(s):  
V. V. Kniaz ◽  
V. A. Mizginov ◽  
L. V. Grodzitkiy ◽  
N. A. Fomin ◽  
V. A. Knyaz
Keyword(s):  
Optical Flow ◽  
3D Model ◽  
Stereo Matching ◽  
Structured Light ◽  
Primary Source ◽  
Ground Truth ◽  
Complex Surface ◽  
Systems Model ◽  
Structured Light Scanner ◽  
Starting Point

Abstract. Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation. While more than 20 companies develop commercially available structured light scanners, structured light technology accuracy has limitations for fast systems. Model surface discrepancies often present if the texture of the object has severe changes in brightness or reflective properties of its texture. The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture. These errors result in ridge-like structures on the surface of the reconstructed 3D model. This paper is focused on the development of a deep neural network LineMatchGAN for error reduction in 3D models produced by a structured light scanner. We use the pix2pix model as a starting point for our research. The aim of our LineMatchGAN is a refinement of the rough optical flow A and generation of an error-free optical flow B̂. We collected a dataset (which we term ZebraScan) consisting of 500 samples to train our LineMatchGAN model. Each sample includes image sequences (Sl, Sr), ground-truth optical flow B and a ground-truth 3D model. We evaluate our LineMatchGAN on a test split of our ZebraScan dataset that includes 50 samples. The evaluation proves that our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels.

Measuring Leaf Areas with a Structured-Light 3D Scanner.

2014 ◽  
Vol 47 (3) ◽  
pp. 232-238
Author(s):  
Kyong-Hee Nam ◽  
◽  
Eun Mi Ko ◽  
Saeromi Mun ◽  
Chang-Gi Kim
Keyword(s):  
Structured Light ◽  
3D Scanner

A robust automatic registration method for hand-held structured light 3D scanner

2014 ◽  
Author(s):  
Guomin Zhan ◽  
Mengqi Wu ◽  
Kai Zhong ◽  
Zhongwei Li ◽  
Yusheng Shi
Keyword(s):  
Structured Light ◽  
Automatic Registration ◽  
3D Scanner ◽  
Registration Method

scholarly journals Aggregate Geometry Generation Method Using a Structured Light 3D Scanner, Spherical Harmonics–Based Geometry Reconstruction, and Placing Algorithms for Mesoscale Modeling of Concrete

2021 ◽  
Vol 33 (8) ◽  
pp. 04021198
Author(s):  
Petikirige Sadeep Madhushan Thilakarathna ◽  
Shanaka Kristombu Baduge ◽  
Priyan Mendis ◽  
Egodawaththa Ralalage Kanishka Chandrathilaka ◽  
Vanissorn Vimonsatit ◽  
...  
Keyword(s):  
Spherical Harmonics ◽  
Structured Light ◽  
Mesoscale Modeling ◽  
3D Scanner ◽  
Geometry Reconstruction

scholarly journals Single-Shot Structured Light Sensor for 3D Dense and Dynamic Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1094 ◽  
Author(s):  
Feifei Gu ◽  
Zhan Song ◽  
Zilong Zhao
Keyword(s):  
Stereo Matching ◽  
Speckle Pattern ◽  
Structured Light ◽  
Optical Element ◽  
Depth Map ◽  
Point Clouds ◽  
Calibration Method ◽  
Acquisition Time ◽  
Single Shot ◽  
Dynamic Reconstruction

Structured light (SL) has a trade-off between acquisition time and spatial resolution. Temporally coded SL can produce a 3D reconstruction with high density, yet it is not applicable to dynamic reconstruction. On the contrary, spatially coded SL works with a single shot, but it can only achieve sparse reconstruction. This paper aims to achieve accurate 3D dense and dynamic reconstruction at the same time. A speckle-based SL sensor is presented, which consists of two cameras and a diffractive optical element (DOE) projector. The two cameras record images synchronously. First, a speckle pattern was elaborately designed and projected. Second, a high-accuracy calibration method was proposed to calibrate the system; meanwhile, the stereo images were accurately aligned by developing an optimized epipolar rectification algorithm. Then, an improved semi-global matching (SGM) algorithm was proposed to improve the correctness of the stereo matching, through which a high-quality depth map was achieved. Finally, dense point clouds could be recovered from the depth map. The DOE projector was designed with a size of 8 mm × 8 mm. The baseline between stereo cameras was controlled to be below 50 mm. Experimental results validated the effectiveness of the proposed algorithm. Compared with some other single-shot 3D systems, our system displayed a better performance. At close range, such as 0.4 m, our system could achieve submillimeter accuracy.

GPU-based real-time structured light 3D scanner at 500 fps

2012 ◽  
Author(s):  
Hao Gao ◽  
Takeshi Takaki ◽  
Idaku Ishii
Keyword(s):  
Real Time ◽  
Structured Light ◽  
3D Scanner

Structured Light Field Generated by Two Projectors for High-Speed Three Dimensional Measurement

2016 ◽  
Vol 28 (4) ◽  
pp. 523-532 ◽  
Author(s):  
Akihiro Obara ◽  
◽  
Xu Yang ◽  
Hiromasa Oku ◽  
Keyword(s):  
High Speed ◽  
Light Field ◽  
Stereo Matching ◽  
Structured Light ◽  
Three Dimensional ◽  
Depth Estimation ◽  
Image Features ◽  
Depth Information ◽  
Speed Tracking ◽  
Three Dimensional Measurement

[abstFig src='/00280004/10.jpg' width='300' text='Concept of SLF generated by two projectors' ] Triangulation is commonly used to restore 3D scenes, but its frame of less than 30 fps due to time-consuming stereo-matching is an obstacle for applications requiring that results be fed back in real time. The structured light field (SLF) our group proposed previously reduced the amount of calculation in 3D restoration, realizing high-speed measurement. Specifically, the SLF estimates depth information by projecting information on distance directly to a target. The SLF synthesized as reported, however, presents difficulty in extracting image features for depth estimation. In this paper, we propose synthesizing the SLF using two projectors with a certain layout. Our proposed SLF’s basic properties are based on an optical model. We evaluated the SLF’s performance using a prototype we developed and applied to the high-speed depth estimation of a target moving randomly at a speed of 1000 Hz. We demonstrate the target’s high-speed tracking based on high-speed depth information feedback.

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