Automatic reconstruction of fully volumetric 3D building models from oriented point clouds

Abstract

A B S T R A C TWe present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds.

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

“…The RANSAC method, proposed in [32], was employed in [36,41,42] with thresholds tuned to the specific dataset to extract planar surfaces of existing indoor rooms before semantic object extraction. In [36], planar segments whose normal vector was parallel and perpendicular to the x−y plane were then considered as floor/ceilings and walls, respectively.…”

“…In [36], planar segments whose normal vector was parallel and perpendicular to the x−y plane were then considered as floor/ceilings and walls, respectively. In [42], slabs and walls were detected when two proximate planar surfaces with parallel normal vector in opposing directions were found. Reference [37] proposed a semiautomated method for the generation of BIM models of existing indoor buildings.…”

“…For each room, walls were differentiated from occlusions based on the points' proximity to both the ceiling and floor. Proximate parallel walls of two adjacent rooms were then labeled as one wall (similar to [42]). In [44], the histogram of point height was also used to determine points of the same floor level in existing indoor buildings through some predefined bin size and prior knowledge of the thickness of the slab.…”

“…Many previous studies focused on the extraction of architectural and exterior components, such as walls and windows, from existing buildings (after completion of construction) [36][37][38][40][41][42]44]. However, point clouds acquired from construction sites contain outliers due to dust, occlusions, and moving objects, which requires additional robust outlier removal procedures [22].…”

TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

“…The topology completeness may be preserved taking the cost of geometry precision or vice versa in common boundary normalization strategies. To find a solution with a good trade-off between quality and compactness, we resort to integer linear programming [45] to select the optimal edges out of the over-complete set of the outline candidates composed of a loop string of boundary lines L bdry , image lines L image and intersection lines L ints extracted previously.…”