Unorganized point classification for robust NURBS surface reconstruction using a point-based neural network

Song, Jinho; Lee, Junhee; Ko, Kwang Hee; Kim, Won-Don; Kang, Tae‐Won; Kim, Jeung-Youb; Nam, Jong-Ho

doi:10.1093/jcde/qwaa086

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…Lee et al [ 18 ] used as-build model to reconstruct the map of plant pipeline. Song et al [ 19 ] used non-uniform rational B-spline surface reconstruction with neural network PointNet.…”

Section: Proposed Methodsmentioning

confidence: 99%

3D Reconstruction Using 3D Registration-Based ToF-Stereo Fusion

Jung

Lee

et al. 2022

Sensors

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Depth sensing is an important issue in many applications, such as Augmented Reality (AR), eXtended Reality (XR), and Metaverse. For 3D reconstruction, a depth map can be acquired by a stereo camera and a Time-of-Flight (ToF) sensor. We used both sensors complementarily to improve the accuracy of 3D information of the data. First, we applied a generalized multi-camera calibration method that uses both color and depth information. Next, depth maps of two sensors were fused by 3D registration and reprojection approach. Then, hole-filling was applied to refine the new depth map from the ToF-stereo fused data. Finally, the surface reconstruction technique was used to generate mesh data from the ToF-stereo fused pointcloud data. The proposed procedure was implemented and tested with real-world data and compared with various algorithms to validate its efficiency.

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“…Lee et al [ 18 ] used as-build model to reconstruct the map of plant pipeline. Song et al [ 19 ] used non-uniform rational B-spline surface reconstruction with neural network PointNet.…”

Section: Proposed Methodsmentioning

confidence: 99%

3D Reconstruction Using 3D Registration-Based ToF-Stereo Fusion

Jung

Lee

et al. 2022

Sensors

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“…These measures are typically utilized to examine the performance in many classification problems [35], [36], [37]. The F1 metric is the weighted average of precision and recall scores.…”

Section: Accuracy =mentioning

confidence: 99%

Machine Learning-Based Day-Ahead Prediction of Price-Setting Scheduled Energy in the Korean Electricity Trading Mechanism

2023

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Power generation companies, which participate in the electricity trading mechanism, need to determine optimal capacity bidding strategy to maximize their operational profit in Korea. Since the Price-setting Power Generation Schedule determines the profit of power generators, it is important to predict Price-setting Scheduled Energy during the trading day before the bidding phase. We propose a methodology for predicting the Pricing-setting Scheduled Energy from the power exchange without optimizing it. Instead of simulating the planning process, machine learning algorithms are applied and compared in the process of predicting the Pricing-setting Scheduled Energy. The input variables consist of seasonal and price information including calendar, fuel cost, and system marginal price. Three categories of machine learning (ML) algorithms including single, bagging and boosting approaches are implemented and tested to compare their performances. The computational experiments show that ML algorithms with price variable are shown to be better in terms of the considered measures. In addition, boosting approach is more effective than single and bagging approaches.

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“…There is improved accuracy and speed of fitting NURBS surfaces [26,27]. Jinho Song et al divide unorganized points into boundary points and internal points using a deep neural network, in order to facilitate more explicit parameterization of boundary points during NURBS modeling [28]. Deming Kong et al improved the current NURBS free-form surface construction method based on discrete stationary wavelet transform.…”

Section: Literature Reviewmentioning

confidence: 99%

A Reverse Modeling Method Based on CAD Model Prior and Surface Modeling

Fan

Wang

et al. 2022

Machines

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Reverse engineering is an important process of real model digitization. However, the existing methods are limited by the characteristics of the modeling object; the modeling is low efficient, has poor versatility, and unstable accuracy. To complete the reverse engineering of a real workpiece with irregular deformation based on the ideal computer-aided design (CAD) model, a high-precision reverse engineering method of the workpiece based on the CAD model prior was presented. Through the registration of the ideal CAD model and the point cloud model of the real workpiece, the geometric feature position information and feature constraint information contained in the CAD model are transmitted to the modeling process, which helps to improve the accuracy and efficiency of reverse engineering. This method is applied to the reverse engineering process of the engine compartment with slight irregular deformation, the modeling accuracy reaches 0.04 mm, and the high-precision reverse engineering of the engine compartment is successfully realized.

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Unorganized point classification for robust NURBS surface reconstruction using a point-based neural network

Cited by 9 publications

References 17 publications

3D Reconstruction Using 3D Registration-Based ToF-Stereo Fusion

3D Reconstruction Using 3D Registration-Based ToF-Stereo Fusion

Machine Learning-Based Day-Ahead Prediction of Price-Setting Scheduled Energy in the Korean Electricity Trading Mechanism

A Reverse Modeling Method Based on CAD Model Prior and Surface Modeling

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