Most frequent surface shapes of man-made constructions are planar surfaces. Discovering those surfaces is a big step toward extracting as-built/-is construction information from 3D point cloud. In this paper, a real-coded genetic... more
Most frequent surface shapes of man-made constructions are
planar surfaces. Discovering those surfaces is a big step toward extracting as-built/-is construction information from 3D point cloud. In this paper, a real-coded genetic algorithm (GA) formulation for planar surfaces recognition in 3D point clouds is presented. The algorithm developed based on a multistage approach; thereby, it finds one planar surface (part of solution) at each stage. In addition, the logarithmically proportional objective function that is used in this approach can adapt itself to scale and spatial density of the point cloud. We tested the proposed application on a synthetic point cloud containing several planar surfaces
with different shapes, positions, and with a wide variety of sizes. The results obtained showed that the proposed method is capable to find all plane’s configurations of flat surfaces with a minor distance to the actual configurations.
planar surfaces. Discovering those surfaces is a big step toward extracting as-built/-is construction information from 3D point cloud. In this paper, a real-coded genetic algorithm (GA) formulation for planar surfaces recognition in 3D point clouds is presented. The algorithm developed based on a multistage approach; thereby, it finds one planar surface (part of solution) at each stage. In addition, the logarithmically proportional objective function that is used in this approach can adapt itself to scale and spatial density of the point cloud. We tested the proposed application on a synthetic point cloud containing several planar surfaces
with different shapes, positions, and with a wide variety of sizes. The results obtained showed that the proposed method is capable to find all plane’s configurations of flat surfaces with a minor distance to the actual configurations.
Research Interests:
3D laser scanning is becoming a standard technology to generate building models of a facility’s as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating... more
3D laser scanning is becoming a standard technology
to generate building models of a facility’s as-is condition.
Since most constructions are constructed upon planar
surfaces, recognition of them paves the way for automation
of generating building models. This paper introduces a new
logarithmically proportional objective function that can be
used in both heuristic and metaheuristic (MH) algorithms to
discover planar surfaces in a point cloud without exploiting
any prior knowledge about those surfaces. It can also adopt
itself to the structural density of a scanned construction. In
this paper, a metaheuristic method, genetic algorithm (GA), is
used to test this introduced objective function on a synthetic
point cloud. The results obtained show the proposed method
is capable to find all plane configurations of planar surfaces
(with a wide variety of sizes) in the point cloud with a minor
distance to the actual configurations.
to generate building models of a facility’s as-is condition.
Since most constructions are constructed upon planar
surfaces, recognition of them paves the way for automation
of generating building models. This paper introduces a new
logarithmically proportional objective function that can be
used in both heuristic and metaheuristic (MH) algorithms to
discover planar surfaces in a point cloud without exploiting
any prior knowledge about those surfaces. It can also adopt
itself to the structural density of a scanned construction. In
this paper, a metaheuristic method, genetic algorithm (GA), is
used to test this introduced objective function on a synthetic
point cloud. The results obtained show the proposed method
is capable to find all plane configurations of planar surfaces
(with a wide variety of sizes) in the point cloud with a minor
distance to the actual configurations.
