Author(s):  
F. Di Paola ◽  
L. Inzerillo

This paper presents a pipeline that has been developed to acquire a shape with particular features both under the geometric and radiometric aspects. In fact, the challenge was to build a 3D model of the black Stone of Palermo, where the oldest Egyptian history was printed with the use of hieroglyphs. The dark colour of the material and the superficiality of the hieroglyphs' groove have made the acquisition process very complex to the point of having to experiment with a pipeline that allows the structured light scanner not to lose the homologous points in the 3D alignment phase. For the texture reconstruction we used a last generation smartphone.


Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4819
Author(s):  
Yikang Li ◽  
Zhenzhou Wang

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.


2006 ◽  
Author(s):  
Qinghua Wu ◽  
Wei Li ◽  
Wenjun Wu ◽  
Ming Zhong ◽  
Tao He ◽  
...  

2009 ◽  
Vol 36 (9) ◽  
pp. 2018-2023 ◽  
Author(s):  
Laura Niven ◽  
Teresa E. Steele ◽  
Hannes Finke ◽  
Tim Gernat ◽  
Jean-Jacques Hublin

2018 ◽  
Vol 26 (6) ◽  
pp. 7598 ◽  
Author(s):  
Zewei Cai ◽  
Xiaoli Liu ◽  
Xiang Peng ◽  
Bruce Z. Gao

2018 ◽  
Vol 14 (6) ◽  
pp. 457-460 ◽  
Author(s):  
Li-mei Song ◽  
Wen-wei Lin ◽  
Yan-gang Yang ◽  
Xin-jun Zhu ◽  
Qing-hua Guo ◽  
...  

Author(s):  
V. V. Kniaz ◽  
V. A. Mizginov ◽  
L. V. Grodzitkiy ◽  
N. A. Fomin ◽  
V. A. Knyaz

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.


2022 ◽  
Author(s):  
Haiyun Guo ◽  
Haowen Zhou ◽  
Partha Banerjee

2018 ◽  
Vol 47 (11) ◽  
pp. 1117004 ◽  
Author(s):  
丁 超 Ding Chao ◽  
唐力伟 Tang Liwei ◽  
曹立军 Cao Lijun ◽  
邵新杰 Shao Xinjie ◽  
邓士杰 Deng Shijie

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