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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - 3D Point Cloud Coarse Registration based on Convex Hull Refined by ICP and NDT
摘要： Non-rigid registration is a crucial step for many applications such as motion tracking, model retrieval, and object recognition. The accuracy of these applications is highly dependent on the initial position used in registration step. In this paper we propose a novel Convex Hull Aided Coarse Registration refined by two algorithms applied on projected points.Firstly,the proposed approach uses a statistical method to find the best plane that represents each point cloud. Secondly, all the points of each cloud are projected onto the corresponding planes. Then, two convex hulls are extracted from the two projected point sets and then matched optimally. Next, the non-rigid transformation from the reference to the model is robustly estimated through minimizing the distance between the matched point's pairs of the two convex hulls.Finally, this transformation estimation is refined by two methods. The first one is the refinement of coarse registration by Iterative Closest Point (ICP). The second one consists of the refinement of coarse registration by the Normal Distribution Transform (NDT). An experimental study ,carried out on several clouds, shows that the refinement of coarse registration with ICP gives, in the most cases, a better result than refinement with NDT.
关键词： Iterative Closest Point (ICP),Convex Hull,Normal Distribution Transform (NDT),Non rigid registration,3D point cloud,Principal Component Analysis (PCA)
更新于2025-09-23 15:22:29
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Prediction of Selective Laser Melting Part Quality Using Hybrid Bayesian Network
摘要： Additive manufacturing (AM) is gaining popularity because of its ability to manufacture complex parts in less time. Despite recent research involving designs of experiments (DOEs) to characterize the relationships between some AM process parameters and various part quality characteristics, to date, there seems to be no universally accepted comprehensive model that relates process parameters to part quality. In this paper, to support the goal of manufacturing parts right the first time, a Bayesian network in continuous domain is developed which relates four process parameters (laser power, scan speed, hatch spacing, and layer thickness) and five part quality characteristics (density, hardness, top layer surface roughness, ultimate tensile strength in the build direction and ultimate tensile strength perpendicular to the build direction). A machine learning algorithm is used to train the network on a database mined from a large number of publications with experimental data from parts built using 316L with selective laser melting. Using this Bayesian network, the user is able to enter a value for one or more known nodes or variables, and the network provides predictions on all the remaining nodes in the form of probability distributions. A method is developed whereby the user inputs are checked for reasonableness using an ??-dimensional convex hull, and if necessary a recommendation is returned based on user-defined weights. The network is validated by retaining a subset of the training data for testing and comparing the network’s predictions to the known values. Accuracy is optimized by continually re-training the network using parts built with a specific machine of interest. The industrial relevance of this research is outlined with respect to four current challenges in AM, including the length of time to determine optimal process parameters for a new machine, ability to organize relevant knowledge, quantification of machine variability, and transfer of knowledge to new operators.
关键词： Hybrid Bayesian Network,Selective Laser Melting Part Quality,Powder Bed Fusion,??-Dimensional Convex Hull,Predictive Model
更新于2025-09-16 10:30:52
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Knowledge Discovery in Nanophotonics Using Geometric Deep Learning
摘要： We present here a distinctive approach for using the intelligence aspects of artificial intelligence for knowledge discovery rather than the conventional task of device optimization in electromagnetic (EM) nanostructures. This approach uses training data obtained through full-wave EM simulations of a series of nanostructures to train geometric deep learning algorithms to assess the range of feasible responses as well as the feasibility of a desired response from a class of nanophotonic structures. To facilitate the knowledge discovery and reduce the computation complexity, our approach combines the dimensionality reduction technique (using an autoencoder) with convex-hull and one-class support-vector-machine (SVM) algorithms to find the range of the feasible responses in the latent (or the reduced) response space of the EM nanostructure. We show that by using a small set of training instances (compared to all possible structures), our approach can provide better than 95% accuracy in assessing the feasibility of a given response. More importantly, the one-class SVM algorithm can be trained to provide the degree of feasibility (or unfeasibility) of a response from a given nanostructure. This important information can be used to modify the initial structure to an alternative one that can enable an initially unfeasible response. To show the applicability of our approach, we apply it to two important classes of binary metasurfaces (MSs), formed by an array of plasmonic nanostructures, and periodic MSs formed by an array of dielectric nanopillars. In addition to theoretical results, we show the experimental results obtained by fabricating several MSs of the second class. Our theoretical and experimental results confirm the unique features of this approach for knowledge discovery in nanophotonics applications.
关键词： convex-hull,one-class SVM,geometric deep learning,knowledge discovery,nanophotonics,autoencoder,electromagnetic nanostructures
更新于2025-09-12 10:27:22
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High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry
摘要： Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In our study, we focused on the reconstruction of trees separately within the forest stand. We investigated the influence of camera lens, tree species, and height of diameter on the accuracy of the tree perimeter and diameter estimation. Furthermore, we investigated the variance of the perimeter and diameter reference measurements. We chose four tree species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst. and Abies alba Mill.). The perimeters and diameters were measured at three height levels (0.8 m, 1.3 m, and 1.8 m) and two types of lenses were used. The data acquisition followed a circle around the tree at a 3 m radius. The highest accuracy of the perimeter estimation was achieved when a fisheye lens was used at a height of 1.3 m for Fagus sylvatica (root mean square error of 0.25 cm). Alternatively, the worst accuracy was achieved when a non-fisheye lens was used at 1.3 m for Quercus petraea (root mean square error of 1.27 cm). The tree species affected the estimation accuracy for both diameters and perimeters.
关键词： close-range photogrammetry,fisheye lens,trunk perimeter,circle fitting,trunk diameter,convex hull,point cloud
更新于2025-09-09 09:28:46