Towards the automatic 3D parametrization of non-planar surfaces from point clouds in HBIM applications

Producción Científica3D laser scanning and photogrammetric 3D reconstruction generate point clouds from which the geometry of BIM models can be created. However, a few methods do this automatically for concrete architectural elements, but in no case for the entirety of heritage assets. A novel procedure for the automatic recognition and parametrization of non-planar surfaces of heritage immovable assets is presented using point clouds as raw input data. The methodology is able to detect the most relevant architectural features in a point cloud and their interdependences through the analysis of the intersections of related elements. The non-planar surfaces detected, mainly cylinders, are studied in relation to the neighbouring planar surfaces present in the cloud so that the boundaries of both the planar and the non-planar surfaces are accurately defined. The procedure is applied to the emblematic Castle of Torrelobatón, located in Valladolid (Spain) to allow the cataloguing of required elements, as illustrative example of the European defensive architecture from the Middle age to the Renaissance period. Results and conclusions are reported to evaluate the performance of this approach

AUTOMATED DETECTION AND LAYOUT REGULARIZATION OF SIMILAR FEATURES IN INDOOR POINT CLOUD

Automated identification of high-level structures in unorganized point cloud of indoor spaces Indoor space is an important aspect of scene analysis that provides essential information for many applications, such as building digitization, indoor navigation and evacuation route planning. In addition, detection of repetition and regularities in the organization indoor environments, such as rooms, can be used to provide a contextual relationship in the reconstruction phase. However, retrieving high-level information is a challenging task due to the unorganized nature of the raw data, poor-quality of the input data that are in many cases contaminated with noise and outliers. in point benefit from the apparent regularities and strong contextual relationships in façades. The main observation exploited in this paper is the fact that building indoor is generally constituted by a set of basic shapes repeated several times in regular layouts. Building elements can be considered as similar if they share a set of features and elements in an idealized layout exhibiting some regularities. Starting from this main assumption a recursive adaptive partitioning of the indoor point cloud is carried out to automatically derive a flexible and hierarchical 3D representation of the building space. The presented methodology is tested on a synthetic dataset with Gaussian noise. The reconstructed pattern shows a close correspondence with the synthetic one showing the viability of the proposed approach

Architecting net zero: from drawings to bytes

Every profession across the globe has been affected by computers in the last decades. Most of the day-to-day jobs are about creating and managing data and the processes include inputs, storage, transfer and output. The construction industry is no exception and has been affected by modern processes. Besides, the entire ecosystem of the construction industry from material manufacturing to usage and demolition is a carbon emitter. The role of the industry in achieving global sustainability targets is significant and the overall construction ecosystem seems to be evolving toward net-zero targets and various tools from design to construction are contributing to this goal. This research surveys the existing literature on significant data generators in the construction, and management systems and how data can be used or are used for net-zero delivery of buildings in the UK. It demonstrates a path to move away from analogy and intuition in building design to a path that is inspired by approved data-driven methods. This study highlights the lack of universal protocols in data management especially in net-zero delivery, the lack of clarity on the required data to effectively reuse building components for lower emissions and most importantly how disruptive industry 5.0 could be when interoperability issues are unsolved among AEC professionals and cloud-based data sharing is still advancing

A semi-automated method of building element retrieval from point cloud

3D point cloud data can be utilized for site inspection and reverse engineering of building models. However, conventional methods for building element retrieval require a database of 3D CAD or BIM models which are unsuitable for the case of historical buildings without as-planned models or temporary structures that are not in the pre-built model. Thus, this paper proposes a semi-automated method to efficiently retrieve duplicate building elements without these constraints. First, the point cloud is processed with a pre-trained deep feature extractor to generate a 50-dimensional feature vector for each point. Next, the point cloud is segmented through feature clustering and region growing algorithms, then displayed on a user interface for selection. Lastly, the selected exemplar is provided as input to a peak-finding algorithm to determine positive matches. The results show the proposed method gets the average rates above 90% of precision and recall scores of each point cloud dataset. The proposed method can distinguish the correct building elements form the similarly-shaped candidates and complex building elements. In terms of the applicability, the study shows the proposed method has a certain tolerance of error with different selected instances or boundaries of the selected exemplar and voxel grid resolution. On the other hand, the actual computation time is reasonably fast and efficient.M.S