On Reconstructing 3D Feature Boundaries

Abstract

Raw point data collected by 3D scanning techniques are usually rendered by building an interpolating robust polygonal mesh. This approach is accurate and fast but provides no means for large scale subsequent modifications. Only local interactive or non-interactive tools are provided that are usually targeted to correcting small imperfections and eliminating noise effects. CAD applications require robust and editable CAD models to support processes such as reproduction, design modification and redesign. In this paper we present a curve approximation method used in a feature-based approach to building feature-based CAD models from 3D point clouds. This approach is based on discovering features on the point cloud by detecting local changes in the morphology of the point cloud. This results in a number of regions that represent object features. The boundaries of the features are approximated by a collection of piecewise cubic rational Bezier curves that best fit the detected border point cloud and are G1 continuous.

A parametric feature-based approach to reconstructing traditional filigree jewelry

“…These shapes may be derived after preprocessing [10] by curve fitting on 2D or 3D points (see [11]) or by deriving 3D custom surfaces by constrained morphing [12].…”

“…By detecting and segmenting the point cloud into feature regions and boundary sets, we are able to reconstruct boundary contours for each area using curve approximation methods, such as [11]. Reconstructed boundaries are used in conjunction with symmetry detection techniques for adapting and placing design elements.…”

Data fitting by G1 rational cubic Bézier curves using harmony search

“…The error of the approximation is controlled by the size of its tetrahedron, which converges to zero by subdividing the curve segments. Stamati and Fudos [20] presented a fast curve approximation method that approximates raw data with cubic rational Be´zier curves. The approach combines least squares approximation with continuity constraints to ensure G 1 continuity between neighbouring curves.…”

Building Editable Free-form Models from Unstructured Point Clouds

“…The point cloud may be acquired for example with the use of a 3D laser scanner [6], or by identifying feature points on multi-camera images [8], [16], or even computerized tomography when it comes to medical applications [15], [20]. For processing the point cloud, various methods have been proposed, which include slicing the point cloud into cross-sections [15], or patches [19], or treating the point cloud as a whole [1].…”