Computational cost of GNG3D algorithm for mesh simplification

In this paper we present a study of the computational cost of the GNG3D algorithm for mesh optimization. This algorithm has been implemented taking as a basis a new method which is based on neural networks and consists on two differentiated phases: an optimization phase and a reconstruction phase. The optimization phase is developed applying an optimization algorithm based on the Growing Neural Gas model, which constitutes an unsupervised incremental clustering algorithm. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The computational cost of both phases is calculated, showing some examples.

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations.

The Impact of BIM on the Distribution of Cost and Return on Investment in UK Construction Projects

It has been widely documented that when Building Information Modelling (BIM) is used, there is a shift in effort to the design phase. Little investigation into the impact of this shift in effort has been done and how it impacts on costs. It can be difficult to justify the increased expenditure on BIM in a market that is heavily driven by costs. There are currently studies attempting to quantify the return on investment (ROI) for BIM for which these returns can be seen to balance out the shift in efforts and costs to the design phase. The studies however quantify the ROI based on the individual stakeholder’s investment without consideration for the impact that the use of BIM from their project partners may have on their own profitability. In this study, a questionnaire investigated opinions and experience of construction professionals, representing clients, consultants, designers and contractors, to determine fluctuations in costs by their magnitude and when they occur. These factors were examined more closely by interviewing senior members representing each of the stakeholder categories and comparing their experience in using BIM within environments where their project partners were also using BIM and when they were not. This determined the differences in how the use and the investment in BIM impacts on others and how costs are redistributed. This redistribution is not just through time but also between stakeholders and categories of costs. Some of these cost fluctuations and how the cost of BIM is currently financed are also highlighted in several case studies. The results show that the current distribution of costs set for traditional 2D delivery is hindering the potential success of BIM. There is also evidence that stakeholders who don’t use BIM may benefit financially from the BIM use of others and that collaborative BIM is significantly different to the use of ‘lonely’ BIM in terms of benefits and profitability.