Relief extraction and editing

Bas-reliefs are widely used in the world around us, for example, on coinage, for branding products, and for sculptural decoration. Reverse engineering of reliefs–extracting existing reliefs from input surfaces–makes it possible to apply them to new items; relief editing tools allow modification of reverse-engineered reliefs. This paper presents a novel approach to relief extraction based on differential coordinates, which offers advantages of speed and precise extraction. It also gives the first method in the literature specifically designed for relief editing. The base surface is estimated using normal smoothing and Poisson reconstruction, allowing a relief (which may lie on a smooth or textured input surface) to be automatically extracted by height thresholding. We also provide a range of relief editing tools, also using differential coordinates, permitting both global transformations (translation, rotation, and scaling) of the whole relief, as well as local modifications to the relief. Our relief editing algorithm, unlike generic mesh editing algorithms, is specifically designed to preserve the geometric detail of the relief over the base surface. The effectiveness of our methods is demonstrated on various examples of real industrial interest

Discovering structural regularity in 3D geometry

We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. We describe and classify possible regular structures and present an effective algorithm for detecting such repeated geometric patterns in point- or meshbased models. Our method assumes no prior knowledge of the geometry or spatial location of the individual elements that define the pattern. Structure discovery is made possible by a careful analysis of pairwise similarity transformations that reveals prominent lattice structures in a suitable model of transformation space. We introduce an optimization method for detecting such uniform grids specifically designed to deal with outliers and missing elements. This yields a robust algorithm that successfully discovers complex regular structures amidst clutter, noise, and missing geometry. The accuracy of the extracted generating transformations is further improved using a novel simultaneous registration method in the spatial domain. We demonstrate the effectiveness of our algorithm on a variety of examples and show applications to compression, model repair, and geometry synthesis. © 2008 ACM

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Segmenting periodic reliefs on triangle meshes

Abstract. Decorative reliefs are widely used for e.g. packaging and porcelain design. In periodic reliefs, the relief repeats a pattern, for example all the way around an underlying surface of revolution. Reverseengineering of existing reliefs allows them to be re-applied to different base surfaces; we show here how to segment a single repeat unit of a periodic relief starting from a scanned triangle mesh. We first briefly review how we segment the relief from the background surface using our previous work. The rest of the paper then concentrates on how we extract a single repeat unit from the relief. To do so, the user provides two points on one relief boundary which are in approximate correspondence on consecutive repeats of the relief. We first refine the relative locations of these points, and then determine a third corresponding point using relief boundary information. These are used to determine three initial cutting planes across the relief. Then surface registration strategies are utilised to refine the correspondence between adjacent repeat units. Finally, we refine the exact locations of the cutting planes by considering only surface information close to the cutting planes. This allows a repeat unit of the periodic relief to be extracted. We demonstrate that our algorithm is successful and practical, using various real scanned models: user input can be quite imprecise, and we can cope with hand-made reliefs in which the pattern units are only approximately copies of each other.

Detection and localisation of structural deformations using terrestrial laser scanning and generalised procrustes analysis

One of the most vital duties for engineers is to preserve life and nature by utilising safe designs that take into account environmental standards and monitoring the performance of structures against design criteria. Furthermore, monitoring can be used to determine any required maintenance of an important structure following a catastrophic event. Numerous different techniques and instruments can be employed for such a purpose with different requirements producing different results. For instance, some techniques need to embed sensors inside the building, such as Geotechnical Sensors. Others can offer high quality, but with a low point density and require fixed stations and targets, like Total Stations (TS). In such cases, the location of deformation tends to be known, such as in dams, bridges, and high-rise buildings. However, this is not always the case where it might be hard to expect deformation location as in the case of historic ruins where each part of the structure could be subject to deformation. The challenge in such case is to detect the deformation without any previous knowledge. Remote Sensing (RS) techniques, such as Digital Photogrammetry, Synthetic Aperture Radar (SAR), Interferometric Synthetic Aperture Radar (InSAR), and Terrestrial Laser Scanner (TLS) can be solutions for such an issue. Interestingly, many researchers are focusing on using TLS for monitoring owing to the great spatial resolution system can offer. However, there are three challenges in using TLS in monitoring: the first one is a huge amount of data and the difficulty of handling it; the second one is the difficulty of comparing between two epochs because observations of TLS are not repeatable; and the third issue is the noise which is attached to the data. The first problem is solved by segmentation and point structure while the second and the third ones still need more investigation, although some interesting researches have been done in this area. The aim of this research is to develop a new approach to detect and localise unpredictable deformation. It is based on TLS measurements and Generalised Procrustes Analysis (GPA) techniques to determine deformation vectors, while boxing structure and F-test are used to detect and localise deformation. In summary, after applying this approach, the whole concerned building is represented as parts, for each of which the displacement vector and the deformation probability are estimated. Ultimately, it is possible to monitor any part through different epochs. In addition, through this technique, it is possible to determine deformations - not just between two epochs, but for sequences of them. This can give more reliable results. Four validation experiments have been conducted. The first test was designed to assess the performance of the developed software and to fix some variables. Therefore, it was based on simulated data with controlled white noise, distributed according to the normal distribution, and simulated deformations. The results of this test revealed the success of the proposed algorithm to detect and to localise deformations. In addition, it showed the success when no deformations exist. Furthermore, optimistically, it could observe deformations with magnitude less than the noise level; however, the probability was only 40%. Correspondingly, real scan data with simulated deformations was used in the second test. The purpose of this test is to examine the performance of the proposed method in case of real errors budget. However, the short range of the test (about 10m), a featureless scanned area (wall only), and scanning from one position for all epochs (no need for registration) can reduce errors to a minimum. Results of this test showed the success of the proposed method to detect and localise deformations. Potentially, it can give indications for areas with deformations less than the noise level. Furthermore, results of the proposed method can be considered better than that of CloudCompare software. The third test was conducted to examine the performance of the proposed technique regarding different materials and textures. For this purpose, the Nottingham Geospatial Building (NGB) was selected with more extensive ranges (between 20-25 m). Similar to the second test, all measurements were taken from the same scanner position. To some extent, the proposed technique succeeded to detect and to localise deformations. However, the researcher does not recommend it for monitoring modern and complicated buildings, instead it has been developed for monitoring historic ruins. Finally, the proposed method was applied on the Bellmanpark Limekiln, Clitheroe, Lancashire monitoring project. This is a live project for Historic England and addresses a historic building that currently has some structural issues. The outcome of the proposed method revealed deformations in the faces South East (SE) and North East (NE). From examining these faces, three deformed areas were found: two in the face SE and one in the face NE, which might cause some cracks appeared in these faces. Alternatively, the CloudCompare software has been employed to detect deformation. Although results coincide with the proposed method for detected deformations, it cannot locate these deformations very well because it diffused over a wide area. In addition, it cannot determine actual directions of the deformations unlike the proposed method

Segmenting Periodic Reliefs on Triangle Meshes

Figure 1: Periodic relief segmentation from a porcelain bowl: the bowl; segmented partial relief on mesh; an extracted repeat unit. Decorative reliefs are widely used for e.g. packaging and porcelain design. In periodic reliefs, the relief repeats a pattern, for example all the way around an underlying surface of revolution. Reverseengineering of existing reliefs allows them to be re-applied to different base surfaces; we show here how to segment a single repeat unit of a periodic relief starting from a scanned triangle mesh. We first briefly review how we segment the relief from the background surface using our previous work. The rest of the paper then concentrates on how we extract a single repeat unit from the relief. To do so, the user provides two points on one relief boundary which are in approximate correspondence on consecutive repeats of the relief. We first refine the relative locations of these points, and then determine a third corresponding point using relief boundary information. These are used to determine three initial cutting planes across the relief. Then surface registration strategies are utilised to refine the correspondence between adjacent repeat units. Finally, we refine the exact locations of the cutting planes by considering only surface information close to the cutting planes. This allows a repeat unit of the periodic relief to be extracted. We demonstrate that our algorithm is successful and practical, using various real scanned models: user input can be quite imprecise, and we can cope with hand-made reliefs in which the pattern units are only approximately copies of each other