We propose a fully automated three-step alignment process that leverages the simple geometry of the pivot building and aligns partial xy-projections, identifies the base using z-histograms and aligns the bounding boxes of partial yz-projections. Secondly, in light of the sparseness of some buildings due to partial capture, we propose to use the best-captured building as a pivot to align the entire point cloud. We use the building-pass filter to remove non-building noise and thus extract the buildings, delineate the boundaries of the building after the base is identified and as a post-processing step after the alignment is achieved. Firstly, we show that a BIM-based 3D filter is a versatile tool that can be used at multiple stages of the alignment process. In this work, we address some unique issues that arise in the alignment of multi-building point clouds. Unlike the problem of aligning single buildings, the multi-building scenario is not well-studied. To infer construction progress from these point clouds, they must be aligned with the as-designed BIM model. Recently, automatic close-range capture of the state of large construction sites has become possible through crane and drone-mounted cameras, which results in sizeable, noisy, multi-building as-built point clouds. 2Centre for Product Design and Manufacturing (CPDM), Indian Institute of Science, Bengaluru, IndiaĪutomatic reality capture and monitoring of construction sites can reduce costs, accelerate timelines and improve quality in construction projects.1Department of Civil Engineering, Aalto University, Espoo, Finland.Masood 1 *, Antti Aikala 1, Olli Seppänen 1 and Vishal Singh 2
0 kommentar(er)
