http://www.ri.cmu.edu/pub_files/2011/6/2011-antonio-isarc-3d-modeling-v6.pdf

Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data

Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018

Productive modeling for development of as-built BIM of existing indoor structures

The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.

https://hal.archives-ouvertes.fr/hal-02320638/document

From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

Laser scanners are increasingly used to create semantically rich 3D models of buildings for civil engineering applications such as planning renovations, space usage planning, and building maintenance. Currently these models are created manually – a time-consuming and error-prone process. This paper presents a method to automatically convert the raw 3D point data from a laser scanner positioned at multiple locations throughout a building into a compact, semantically rich model. Our algorithm is capable of identifying and modeling the main structural components of an indoor environment (walls, floors, ceilings, windows, and doorways) despite the presence of significant clutter and occlusion, which occur frequently in natural indoor environments. Our method begins by extracting planar patches from a voxelized version of the input point cloud. We use a conditional random field model to learn contextual relationships between patches and use this knowledge to automatically label patches as walls, ceilings, or floors. Then, we perform a detailed analysis of the recognized surfaces to locate windows and doorways. This process uses visibility reasoning to fuse measurements from different scan locations and to identify occluded regions and holes in the surface. Next, we use a learning algorithm to intelligently estimate the shape of window and doorway openings even when partially occluded. Finally, occluded regions on the surfaces are filled in using a 3D inpainting algorithm. We evaluated the method on a large, highly cluttered data set of a building with forty separate rooms yielding promising results.

We developed an automatic technique for the reconstruction of Manhattan-world interior scenes from point clouds. Our method mainly focuses on the reduction of human intervention in the modelling process, thus it aims to a full automation of the modelling phase with a consequent optimization of the efficiency of the laser-scanning project pipeline. We refer to a reconstruction complexity typical for indoor scenes. The work flow starts with a volume sweep reconstruction of the interior from the three-dimensional point cloud. As result of a discrete translational plane sweep, the input data is segmented into separate point sets including the floor, ceiling and wall points. Consequently, each point is assigned to a surface of the volume. The ground plan contours are extracted with a cell decomposition approach after partitioning the floor surface into rectangular cells of variable size. Only cells considered suitable are added to the ground shape and unified to define the ground plan. Along the ground plan contour, the walls are raised from the floor up to the ceiling level. Finally, the interior model is enhanced by the addition of built-in feature like doors.

/pdf/automatic-3d-modelling-of-indoor-manhattan-world-scenes-from-jej4ymn3ab.pdf

A Budroni

Papers

Automatic 3D modelling of indoor Manhattan-world scenes from laser data