A self-similarity based adaptive steganography for 3D point cloud models

This study considers the generation of virtual, 3D point-cloud models of seven deteriorating historical, agricultural barns in Bulloch County, Georgia, USA, for preservation purposes. The work was completed as a service-learning project in a course on Terrestrial Light Detection and Ranging (T-LiDAR), offered at Georgia Southern University. The resulting models and fly-through videos were donated to Bulloch County Historical Society and to the Georgia Southern Museum, to make them available to the general public and future generations. Additionally, one of the seven barns was selected to be extensively measured to estimate the relative spatial accuracy of all seven resulting 3D point-cloud models, with respect to measurements completed with a highly accurate instrument. Three accurate benchmarks were established around it for georeferencing purposes. The positions of 44 points were measured in the field via an accurate, one- second, robotic total-station (RTS) instrument. Also, the coordinates of the same points were acquired from within georeferenced and non-georeferenced point-cloud models. These points defined 259 distances. They were compared to determine their discrepancy statistics. It was observed that this process produced virtual models with an approximate maximum spatial discrepancy of one-half inch (0.5 in) with respect to measurements performed by a highly accurate RTS device. There were no substantial differences in the relative accuracies of the georeferenced and non-georeferenced models.

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Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM

Computational Intelligence and Neuroscience ◽

10.1155/2021/9927982 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Jingli Wang ◽

Huiyuan Zhang ◽

Jingxiang Gao ◽

Dong Xiao

Keyword(s):

Least Squares ◽

Point Cloud ◽

Cloud Model ◽

Hollow Structure ◽

Total Least Squares ◽

3D Point Cloud ◽

Denoising Method ◽

Least Squares Fitting ◽

Cloud Models ◽

Point Cloud Model

With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very common. However, the truck operation caused the 3D point cloud model of the mining area to contain dust points, and the 3D point cloud model established by the Context Capture modeling software is a hollow structure. The previous point cloud denoising algorithms caused holes in the model. In view of the above problems, this paper proposes the point cloud denoising method based on orthogonal total least squares fitting and two-layer extreme learning machine improved by genetic algorithm (GA-TELM). The steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-TELM to repair holes. The advantages of the proposed method are listed as follows. First, this method could denoise without generating holes, which solves engineering problems. Second, GA-TELM has a better effect in repairing holes compared with the other methods considered in this paper. Finally, this method starts from actual problems and could be used in mining areas with the same problems. Experimental results demonstrate that it can remove dust spots in the flat area of the mine effectively and ensure the integrity of the model.

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Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging

Sensors ◽

10.3390/s19235295 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5295 ◽

Cited By ~ 2

Author(s):

Guoxiang Sun ◽

Yongqian Ding ◽

Xiaochan Wang ◽

Wei Lu ◽

Ye Sun ◽

...

Keyword(s):

Fourier Transform ◽

Point Cloud ◽

Prediction Models ◽

Three Dimensional ◽

Point Clouds ◽

Support Vector ◽

3D Point Cloud ◽

Tomato Plants ◽

Nondestructive Determination ◽

Cloud Models

Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.

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