scholarly journals BIM RECONSTRUCTION: AUTOMATED PROCEDURAL MODELING FROM POINT CLOUD DATA

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 53-60
Author(s):  
M. Bassier ◽  
L. Mattheuwsen ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Information Modeling ◽  
Data Sets ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Structure Information ◽  
Wide Range ◽  
Building Information

Abstract. The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract structure information from point cloud data to create a set of parametric objects. This requires the interpretation of the point cloud data which currently is a manual and labor intensive procedure. Automated processes have to cope with excessive occlusions and clutter in the data sets. To create an as-built BIM, it is vital to reconstruct the building’s structure i.e. wall geometry prior to the reconstruction of other objects.In this work, a novel method is presented to automatically reconstruct as-built BIM for generic buildings. We presented an unsupervised method that procedurally models the geometry of the walls based on point cloud data. A bottom-up process is defined where consecutively higher level information is extracted from the point cloud data using pre-trained machine learning models. Prior to the reconstruction, the data is segmented, classified and clustered to retrieve all the available observations of the walls. The resulting geometry is processed by the reconstruction algorithm. First, the necessary information is extracted from the observations for the creation of parametric solid objects. Subsequently, the final walls are created by updating their topology. The method is tested on a variety of scenes and shows promising results to reliably and accurately create as-built models. The accuracy of the generated geometry is similar to the precision of expert modelers. A key advantage is that that the algorithm creates Revit and Rhino native objects which makes the geometry directly applicable to a wide range of applications.

scholarly journals IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS

2018 ◽  
Vol IV-2 ◽  
pp. 33-39 ◽  
Author(s):  
M. Bassier ◽  
R. Klein ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Initial Point ◽  
Point Clouds ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Building Information ◽  
Wall Geometry

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data.<br>In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

scholarly journals Topology Reconstruction of BIM Wall Objects from Point Cloud Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1800 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Evaluation Framework ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Current State ◽  
Building Information ◽  
Popular Subject ◽  
Best Fit

The processing of remote sensing measurements to Building Information Modeling (BIM) is a popular subject in current literature. An important step in the process is the enrichment of the geometry with the topology of the wall observations to create a logical model. However, this remains an unsolved task as methods struggle to deal with the noise, incompleteness and the complexity of point cloud data of building scenes. Current methods impose severe abstractions such as Manhattan-world assumptions and single-story procedures to overcome these obstacles, but as a result, a general data processing approach is still missing. In this paper, we propose a method that solves these shortcomings and creates a logical BIM model in an unsupervised manner. More specifically, we propose a connection evaluation framework that takes as input a set of preprocessed point clouds of a building’s wall observations and compute the best fit topology between them. We transcend the current state of the art by processing point clouds of both straight, curved and polyline-based walls. Also, we consider multiple connection types in a novel reasoning framework that decides which operations are best fit to reconstruct the topology of the walls. The geometry and topology produced by our method is directly usable by BIM processes as it is structured conform the IFC data structure. The experimental results conducted on the Stanford 2D-3D-Semantics dataset (2D-3D-S) show that the proposed method is a promising framework to reconstruct complex multi-story wall elements in an unsupervised manner.

scholarly journals CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 101-108 ◽  
Author(s):  
M. Bassier ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Conditional Random Field ◽  
Initial Point ◽  
Point Clouds ◽  
The Body ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Wall Geometry

<p><strong>Abstract.</strong> The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is retrieving the proper observations for each object. After segmenting and classifying the initial point cloud, the labeled segments should be clustered according to their respective objects. However, this procedure is challenging due to noise, occlusions and the associativity between different objects. This is especially important for wall geometry as it forms the basis for further BIM reconstruction.</p><p> In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each observation in order to determine which wall it belongs too. The emphasis is on the clustering of highly associative walls as this topic currently is a gap in the body of knowledge. First a set of classified planar primitives is obtained using algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph and thus the most likely configuration of the observations. The experiments prove that the used method is a promising framework for wall clustering from unstructured point cloud data. Compared to a conventional region growing method, the proposed method significantly reduces the rate of false positives, resulting in better wall clusters. A key advantage of the proposed method is its capability of dealing with complex wall geometry in entire buildings opposed to the presented methods in current literature.</p>

scholarly journals Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM

2020 ◽  
Vol 25 ◽  
pp. 173-192 ◽  
Author(s):  
Maarten Bassier ◽  
Meisam Yousefzadeh ◽  
Maarten Vergauwen
Keyword(s):  
Point Cloud ◽  
Real Life ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Empirical Comparison ◽  
Ongoing Research ◽  
Cloud Data ◽  
2D And 3D ◽  
Wall Geometry

As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is still ongoing research with both 2D and 3D approaches being proposed. There currently is a gap in the literature concerning the quality assessment of the created entities. In this research, we present the empirical comparison of both strategies with respect to existing specifications. A 3D and a 2D reconstruction method are implemented and tested on a real life test case. The experiments focus on the reconstruction of the wall geometry from unstructured point clouds as it forms the basis of the model. Both presented approaches are unsupervised methods that segment, classify and create generic wall elements. The first method operates on the 3D point cloud itself and consists of a general approach for the segmentation and classification and a class-specific reconstruction algorithm for the wall geometry. The point cloud is first segmented into planar clusters, after which a Random Forests classifier is used with geometric and contextual features for the semantic labelling. The final wall geometry is created based on the 3D point clusters representing the walls. The second method is an efficient Manhattan-world scene reconstruction algorithm that simultaneously segments and classifies the point cloud based on point feature histograms. The wall reconstruction is considered an instance of image segmentation by representing the data as 2D raster images. Both methods have promising results towards the reconstruction of wall geometry of multi-story buildings. The experiments report that over 80% of the walls were correctly segmented by both methods. Furthermore, the reconstructed geometry is conform Level-of-Accuracy 20 for 88% of the data by the first method and for 55% by the second method despite the Manhattan-world scene assumption. The empirical comparison showcases the fundamental differences in both strategies and will support the further development of these methods.

scholarly journals Point Cloud vs. Mesh Features for Building Interior Classification

2020 ◽  
Vol 12 (14) ◽  
pp. 2224 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen ◽  
Florent Poux
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Information Modeling ◽  
Point Cloud Data ◽  
3D Mesh ◽  
Cloud Data ◽  
And Topology ◽  
Building Information ◽  
Building Components ◽  
The Impact

Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles such as noise, occlusions, sparsity or variance in the density. Alternatively, 3D mesh geometries derived from point clouds benefit from preprocessing routines that can surmount these obstacles and potentially result in more refined geometry and topology descriptions. In this article, we provide a rigorous comparison of both geometries for scene interpretation. We present an empirical study on the suitability of both geometries for the feature extraction and classification. More specifically, we study the impact for the retrieval of structural building components in a realistic environment which is a major endeavor in Building Information Modeling (BIM) reconstruction. The study runs on segment-based structuration of both geometries and shows that both achieve recognition rates over 75% F1 score when suitable features are used.

scholarly journals A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 263
Author(s):  
Munan Yuan ◽  
Xiru Li ◽  
Longle Cheng ◽  
Xiaofeng Li ◽  
Haibo Tan
Keyword(s):  
Bipartite Graph ◽  
Point Cloud ◽  
Graph Matching ◽  
Data Sets ◽  
Graph Structure ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Alignment Process ◽  
Coarse To Fine ◽  
Fine Registration

Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.

scholarly journals An Efficient Filtering Approach for Removing Outdoor Point Cloud Data of Manhattan-World Buildings

2021 ◽  
Vol 13 (19) ◽  
pp. 3796
Author(s):  
Lei Fan ◽  
Yuanzhi Cai
Keyword(s):  
Point Cloud ◽  
Indoor Environment ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
Hole Filling ◽  
Cloud Data ◽  
Indoor Space ◽  
Wide Range ◽  
Data Points ◽  
Filtering Approach

Laser scanning is a popular means of acquiring the indoor scene data of buildings for a wide range of applications concerning indoor environment. During data acquisition, unwanted data points beyond the indoor space of interest can also be recorded due to the presence of openings, such as windows and doors on walls. For better visualization and further modeling, it is beneficial to filter out those data, which is often achieved manually in practice. To automate this process, an efficient image-based filtering approach was explored in this research. In this approach, a binary mask image was created and updated through mathematical morphology operations, hole filling and connectively analysis. The final mask obtained was used to remove the data points located outside the indoor space of interest. The application of the approach to several point cloud datasets considered confirms its ability to effectively keep the data points in the indoor space of interest with an average precision of 99.50%. The application cases also demonstrate the computational efficiency (0.53 s, at most) of the approach proposed.

scholarly journals Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Large Error ◽  
Small Error ◽  
Data Reconstruction ◽  
Point Cloud Data ◽  
Local Convexity ◽  
Cloud Data ◽  
Time Consumption ◽  
Local Connection

Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

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