Abstract
Automatic reality capture and monitoring of construction sites can reduce costs, accelerate timelines and improve quality in construction projects. 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. To infer construction progress from these point clouds, they must be aligned with the as-designed BIM model. Unlike the problem of aligning single buildings, the multi-building scenario is not well-studied. In this work, we address some unique issues that arise in the alignment of multi-building point clouds. Firstly, we show that a BIM-based 3D filter is a versatile tool that can be used at multiple stages of the alignment process. 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. 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 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. Experimental results with crane camera point clouds of a large construction site show that our proposed techniques are fast and accurate, allowing us to estimate the current floor under construction from the aligned clouds and enabling potential slab state analysis. This work contributes a fully automated method of reality capture and monitoring of multi-building construction sites.
Highlights
The need for automating progress monitoring in construction projects is well-established in light of the waste involved in manually inspecting sites and updating records (Golparvar-Fard et al, 2011)
A previous work of ours (Masood et al, 2019) proposed a building extraction strategy based on 3D convex hull volumes of clusters on an as-built point cloud, based on the observation that buildings are larger than non-building elements
Reality capture technologies are improving in level of automation and coverage, which is making it possible to acquire images and 3D point clouds of large, multi-building construction sites
Summary
The need for automating progress monitoring in construction projects is well-established in light of the waste involved in manually inspecting sites and updating records (Golparvar-Fard et al, 2011). An effective method for extracting useful information from as-built point clouds is to align them with the as-designed BIM model (Bosché, 2010). This method has opened up a plethora of applications in construction, BEAM: Multi-Building Extraction and Alignment such as construction progress tracking, safety management and dimensional quality management (Wang and Kim, 2019). The performance of the algorithm is evaluated by comparing it with manually generated ground truth transformations and examining the proportion of the cast-in-place roof slab that can be correctly extracted after alignment This is one of few works that study crane camera point clouds. This paper is organized as follows: section 2 discusses related work; section 3 describes the crane camera dataset; section 4 explains the BEAM algorithm; section 5 presents the results and discussion, including a potential application of mapping the output of BEAM to 2D crane camera images; section 6 discusses some limitations of this work; and section 7 presents the conclusions
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