Survey on 3D Surface Reconstruction

Abstract

The recent advent of increasingly affordable and powerful 3D scanning devices capable of capturing high resolution range data about real-world objects and environments has fueled research into effective 3D surface reconstruction techniques for rendering the raw point cloud data produced by many of these devices into a form that would make it usable in a variety of application domains. This paper, therefore, provides an overview of the existing literature on surface reconstruction from 3D point clouds. It explains some of the basic surface reconstruction concepts, describes the various factors used to evaluate surface reconstruction methods, highlights some commonly encountered issues in dealing with the raw 3D point cloud data and delineates the tradeoffs between data resolution/accuracy and processing speed. It also categorizes the various techniques for this task and briefly analyzes their empirical evaluation results demarcating their advantages and disadvantages. The paper concludes with a cross-comparison of methods which have been evaluated on the same benchmark data sets along with a discussion of the overall trends reported in the literature. The objective is to provide an overview of the state of the art on surface reconstruction from point cloud data in order to facilitate and inspire further research in this area.

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

“…From the work of Khatamian et al in [71], the surface reconstructed can be organized into two categories: explicit and implicit surfaces.…”

“…In parametric surface reconstruction, B-Spline, NURBS, plane, spheres, and ellipsoids are some of the primitive models used to enclose a random set of points to represent surfaces. However, complex surfaces can be hard to represent using this method as a single primitive model as it might not encompass all of the points and might require multiple primitives to represent the object [71].…”

“…The latter surface representation uses a more intuitive technique [71]. Triangulated reconstructions represent surfaces by connecting neighbor points using tethers forming triangles [84].…”

“…The second type of surface reconstruction uses mathematical basis functions to estimate the object's surface based on the input data [71,72]. As such, this method exhibits certain difficulties when representing edges or corners due to the sharp changes not making it suitable for complex surface reconstructions.…”

Segmentation of Change in Surface Geometry Analysis for Cultural Heritage Applications

“…The scanning of real objects is affected by various problems that make it difficult to perform the segmentation without uncertainty. The common factors that produce ambiguity in the recognition process of classification are point location noise [9][10][11], mostly from the uneven part of the surface, the thermal noise of CCD/CMOS detectors, optical phenomena [12], and the coarse representation of continuous surfaces due to triangular approximations [13]. The most common approaches for the geometric segmentation process are edge-based (curvature), region-based (density, smoothness, similarity), model-based, and hybrid methods, where both edge-based and region-based segmentation are used [14].…”