Contour curve reconstruction from cloud data for rapid prototyping

Javidrad, F.; Pourmoayed, A. R.

doi:10.1016/j.rcim.2010.08.008

Cited by 27 publications

(10 citation statements)

References 18 publications

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“…It is not trivial to detect curved surface walls in multi-room indoor environments. To simplify the work of curved surface wall reconstruction, 2D slices are often used [7,16]. Because this work restricts the assumption that walls are orthogonal to the ground, the slice method is proper.…”

Section: Related Workmentioning

confidence: 99%

“…It is difficult to arrange a label to such small cells. Furthermore, it is not trivial to detect curved surface walls in multi-room indoor environments [7,8]. Although slices are often used to simplify detection work, detected curved lines are often scattered and broken into pieces and vary in length, which is not suitable for modeling problems.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Indoor Reconstruction from Point Clouds in Multi-room Environments with Curved Walls

Yang

Zhou

et al. 2019

Sensors

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Recent developments in laser scanning systems have inspired substantial interest in indoor modeling. Semantically rich indoor models are required in many fields. Despite the rapid development of 3D indoor reconstruction methods for building interiors from point clouds, the indoor reconstruction of multi-room environments with curved walls is still not resolved. This study proposed a novel straight and curved line tracking method followed by a straight line test. Robust parameters are used, and a novel straight line regularization method is achieved using constrained least squares. The method constructs a cell complex with both straight lines and curved lines, and the indoor reconstruction is transformed into a labeling problem that is solved based on a novel Markov Random Field formulation. The optimal labeling is found by minimizing an energy function by applying a minimum graph cut approach. Detailed experiments were conducted, and the results indicate that the proposed method is well suited for 3D indoor modeling in multi-room indoor environments with curved walls.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Indoor Reconstruction from Point Clouds in Multi-room Environments with Curved Walls

Yang

Zhou

et al. 2019

Sensors

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“…Liu [3] and Park [4] used a set of line segments to reconstruct profile curve. In some other works [5][6][7][8], researchers used a single B-spline to reconstruct profile curves. Chen et al [9] proposed an algorithm that constructed NURBS curves based on letting inflection points be control points.…”

Section: Introductionmentioning

confidence: 99%

High‐Precision Extraction of Segment Points of 2D Profiles Based on a Dynamic Grid Method

Zhang

et al. 2019

Mathematical Problems in Engineering

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In order to improve the reconstruction precision and performance of 2D profiles, this paper presents a new high-precision extraction method of segment points of 2D profile features. Firstly, we extract the intervals which include initial segment points based on the curvature information of data points. Secondly, a grid is dynamically constructed in the interval and all the nodes of the grid are considered as candidate segment points for an optimization process. Thirdly, the optimization model of each feature curve is constructed on the basis of boundary constraints. Selection of the optimal segment point is related to two factors: the number of control points of B-spline and the total approximation error of all collected points to the fitted curves. Numerical experiments were conducted and the results demonstrate the efficacy of our method in capturing design intent and in industrial applications, compared to existing methods.

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“…The additive manufacturing processing includes slicing the model into a series of thickness layers in Z direction, fabricating each layer and accumulating the layers into a product [2].…”

Section: Introductionmentioning

confidence: 99%