A comparison of hole-filling methods in 3D

This paper presents a review of the most relevant current techniques that deal with hole-filling in 3D models. Contrary to earlier reports, which approach mesh repairing in a sparse and global manner, the objective of this review is twofold. First, a specific and comprehensive review of hole-filling techniques (as a relevant part in the field of mesh repairing) is carried out. We present a brief summary of each technique with attention paid to its algorithmic essence, main contributions and limitations. Second, a solid comparison between 34 methods is established. To do this, we define 19 possible meaningful features and properties that can be found in a generic hole-filling process. Then, we use these features to assess the virtues and deficiencies of the method and to build comparative tables. The purpose of this review is to make a comparative hole-filling state-of-the-art available to researchers, showing pros and cons in a common framework.• Ministerio de Economía y Competitividad: Proyecto DPI2013-43344-R (I+D+i) • Gobierno de Castilla-La Mancha: Proyecto PEII-2014-017-PpeerReviewe

A geometrical-based approach to recognise structure of complex interiors

3D modelling of building interiors has gained a lot of interest recently, specifically since the rise of Building Information Modeling (BIM). A number of methods have been developed in the past, however most of them are limited to modelling non-complex interiors. 3D laser scanners are the preferred sensor to collect the 3D data, however the cost of state-of-the-art laser scanners are prohibitive to many. Other types of sensors could also be used to generate the 3D data but they have limitations especially when dealing with clutter and occlusions. This research has developed a platform to produce 3D modelling of building interiors while adapting a low-cost, low-level laser scanner to generate the 3D interior data. The PreSuRe algorithm developed here, which introduces a new pipeline in modelling building interiors, combines both novel methods and adapts existing approaches to produce the 3D modelling of various interiors, from sparse room to complex interiors with non-ideal geometrical structure, highly cluttered and occluded. This approach has successfully reconstructed the structure of interiors, with above 96% accuracy, even with high amount of noise data and clutter. The time taken to produce the resulting model is almost real-time, compared to existing techniques which may take hours to generate the reconstruction. The produced model is also equipped with semantic information which differentiates the model from a regular 3D CAD drawing and can be use to assist professionals and experts in related fields

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

Improving environment modelling by edge occlusion surface completion

Reconstruction of 3D models from range images usually focuses on complex objects completely contained in the field of view. Using range images to reconstruct a whole environment is challenging because of many occlusions. The focus of this paper is the reconstruction of the corners and edges of partially occluded simple-shape objects like furniture pieces in a indoor scene. Little research has been done on reconstructing obscured surface parts. We introduce a new approach to detect and complete occlusions. The proposed approach is based on good boundary and surface continuation and explores architectural constraints as well. Results on real images confirmed improvement of environment modelling and the perception of the scene objects.

Surface scanning with uncoded structured light sources.

Structured Light Scanners measure the surface of a target object, producing a set of vertices which can be used to construct a three-dimensional model of the surface. The techniques are particularly appropriate for measuring the smoothly undulating, featureless forms which Stereo Vision methods find difficult, and the structured light pattern explicitly gives a dense graph of connected vertices, thus obviating the need for vertex-triangulation prior to surface reconstruction. In addition, the technique provides the measurements almost instantaneously, and so is suitable for scanning moving and non-rigid objects. Because of these advantages there is an imperative to extend the range of scannable surfaces to those including occlusions, which often reduce or prevent successful measurement.This thesis investigates ways of improving both the accuracy and the range of surface types which can be scanned using structured light techniques, extending current research by examining the role of occlusions and geometric constraints, and introducing novel algorithms to solve the Indexing Problem. The Indexing Problem demands that for every pattern element in the projected image, its counterpart, reflected from the surface of the target object, must be found in the recorded image, and most researchers have declared this problem to be intractable without resorting to coding schemes which uniquely identify each pattern element. The use of uncoded projection patterns, where the pattern elements are projected without any unique identification, has two advantages: firstly it provides the densest possible set of measured vertices within a single video timeframe, and secondly it allows the investigation of the fundamental problems without the distraction of dealing with coding schemes. These advantages educe the general strategy adopted in this thesis, of attempting to solve the Indexing Problem using uncoded patterns, and then adding some coding where difficulties still remain.In order to carry out these investigations it is necessary to precisely measure the system and its outputs, and to achieve this requirement two scanners have been built, a Single Stripe Scanner and a Multiple Stripe Scanner. The Single Stripe Scanner introduces the geometric measurement methods and provides a reference output which matches the industry standard; the Multiple Stripe Scanner then tests the results of the investigations and evaluates the success of the new algorithms and constraints. In addition, some of the investigations are tested theoretically, by using synthetic data and by the solution of geometric diagrams.These evaluations of success show that, if occlusions are not present in the recorded data, the Indexing Problem can often be completely solved if the new indexing algorithms and geometric constraints are included. Furthermore, while there are some cases where the Indexing Problem cannot be solved without recourse to a coding scheme, the addition of occlusion detection in the algorithms greatly improves the indexing accuracy and therefore the successful measurement of the target surface