Using Laser Measuring and SFM Algorithm for Fast 3D Reconstruction of Objects

Li, Shuaihao; He, Yanxiang; Li, Qianqian; Chen, Min

doi:10.1007/s10946-018-9756-7

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…Gobbi B et al [15] used SFM technology to model forests in three dimensions and derive ecological indicators describing forest conditions. Li S et al [16] proposed a fast and integrated 3D reconstruction method for small static objects using the laser scanning and SFM algorithm. Experimental results showed that this method improves the speed, accuracy, completeness, and visual effect of 3D reconstruction of small objects.…”

Section: Introductionmentioning

confidence: 99%

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Zhang

Huo

Liu

et al. 2023

Forests

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Rain spots on green plum are superficial micro-defects. Defect detection based on a two-dimensional image is easily influenced by factors such as placement position and light and is prone to misjudgment and omission, which are the main problems affecting the accuracy of defect screening of green plum. In this paper, using computer vision technology, an improved structure from motion (SFM) and patch-based multi-view stereo (PMVS) algorithm based on similar graph clustering and graph matching is proposed to perform three-dimensional sparse and dense reconstruction of green plums. The results show that, compared with the traditional algorithm, the running time of this algorithm is lower, at only 26.55 s, and the mean values of camera optical center error and pose error are 0.019 and 0.631, respectively. This method obtains a higher reconstruction accuracy to meet the subsequent plum micro-defect detection requirements. Aiming at the dense point cloud model of green plums, through point cloud preprocessing, the improved adaptive segmentation algorithm based on the Lab color space realizes the effective segmentation of the point cloud of green plum micro-defects. The experimental results show that the average running time of the improved adaptive segmentation algorithm is 2.56 s, showing a faster segmentation speed and better effect than the traditional K-means and K-means++ algorithms. After clustering the micro-defect point cloud, the micro-defect information of green plums was extracted on the basis of random sample consensus (RANSAC) plane fitting, which provides a theoretical model for further improving the accuracy of sorting the appearance quality of green plums.

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Section: Introductionmentioning

confidence: 99%

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Zhang

Huo

Liu

et al. 2023

Forests

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“…In order to eliminate the error caused by the deflection between the measured system and the co-construction, Jia et al [22] measured the weld by the height value based on the grid laser and the gray value of the gray photo to obtain the 3D contour. Aiming at the problem of low reconstruction accuracy of small objects, Li et al [23] provided a fast and complete 3D reconstruction method based on a laser scanning and SMF algorithm, which improved the speed, accuracy, integrity, and visual effect of 3D modeling of small objects. However, the result of laser measurement is easily affected by noise as the laser line has a certain width, and the fuzzy contour line may lead to a deviation between the reconstructed result and the measured object.…”

Section: Introductionmentioning

confidence: 99%

A Method for Extracting a Laser Center Line Based on an Improved Grayscale Center of Gravity Method: Application on the 3D Reconstruction of Battery Film Defects

Yao,

Wang,

et al. 2023

Applied Sciences

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Extraction of the laser fringe center line is a key step in the 3D reconstruction of linear structured light, the accuracy of which is directly related to the quality of the 3D model. A laser center line extraction method based on an improved gray center of gravity method is proposed to solve the problem of low extraction accuracy. Firstly, a smoothing method is used to eliminate the flat top of the laser line, and the Gaussian curve is adopted to fit the peak position of the curve. Then, the gray threshold is set to automatically extract the laser linewidth, and based on the window opening, the grayscale center of gravity method is improved to extract the coordinates of the center pixel for the second time. Finally, experiments show that the average absolute error of the improved laser line extraction method is 0.026 pixels, which is 2.3 times lower than the gray center of gravity method, 1.9 times lower than the curve fitting method, and the standard error can reach 0.005 pixels. Compared with the gray center of gravity method and the curve fitting method, the influence of gray value change on the center line extraction is more fully considered, and the center of the light strip can be extracted more accurately, achieving sub-pixel accuracy.

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“…According to this method, Lin [9] et al built a 3D desktop scanner for small complex objects, which costs low and can achieve high precision scanning. Li et al [10] also used laser scanning and the structure from motion algorithm to improve the speed, accuracy and integrity of 3D scanning.…”

Section: Introductionmentioning

confidence: 99%

Research on laser stripe characteristics and center extraction algorithm for desktop laser scanner

et al. 2021

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This study presents laser stripe center extraction algorithm for desktop-level 3D laser scanners. The laser stripe center extraction accuracy is an important factor affecting 3D scanning result. Desktop-level devices should have adaptability of a wide range of scanning objects. In this paper, laser stripe energy distribution characteristics with different laser stripe width, ambient light, materials and colors are obtained by experiments. Experiment results show that waveforms of bright spot, low brightness stripe and stripe with large width are complex or easily disturbed, so the center extraction algorithm of them are studied. The extraction effects of extremum method, gradient method and gray centroid method under different conditions are compared. Based on traditional grayscale value, a weighted grayscale value is proposed to extract laser stripe center. Standard deviations of extracted pixel position and fitting pixel position are calculated by different method with different weighted grayscale value. For different conditions, especially for different ambient light intensity, weight matrix plays an important role to extraction result.

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Using Laser Measuring and SFM Algorithm for Fast 3D Reconstruction of Objects

Cited by 10 publications

References 13 publications

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

A Method for Extracting a Laser Center Line Based on an Improved Grayscale Center of Gravity Method: Application on the 3D Reconstruction of Battery Film Defects

Research on laser stripe characteristics and center extraction algorithm for desktop laser scanner

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