Abstract
Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named ‘Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.
Highlights
Digital twin building (DTBs), as real-time ‘as-is’ 3D building models, have attracted great attention of both industry and academy due to the promised applications in the GIScience [1], manufacturing, robotics, mapping, architecture, engineering, construction, and operation (AECO) industries [2,3,4,5], and heritage documentation [6,7]
Many existing methods can be used for digital twin buildings (DTBs); the methods are challenged in practices by (i) the high cost of labor, equipment, and time in data collection for numerous existing buildings [14] and (ii) the computational complexity from self-similar layout patterns [15]
This paper focuses on compensating the low-cost point clouds captured by ubiquitous smartphones with low-cost and widely available 2D computer-aided design (CAD)
Summary
Digital twin building (DTBs), as real-time ‘as-is’ 3D building models, have attracted great attention of both industry and academy due to the promised applications in the GIScience [1], manufacturing , robotics, mapping, architecture, engineering, construction, and operation (AECO) industries [2,3,4,5], and heritage documentation [6,7]. Three long-standing requirements on the roadmap of DTB, i.e., parametric geometry, rich semantics, and realistic appearances, are indispensable for fulfilling the functions of “understanding, learning, and reasoning” [10,11,12,13]. In the design and operation phases, digital models with parametric geometry, rich semantics, and realistic appearances bring practitioners a more comprehensive and thorough understanding of the built environment, as well as solid data support for automation and analytics [4,8]. The 3D data collected by the consumer-level devices often has a quality problem, e.g., noisier or sparser than those produced by professional TLS or MLS equipment. Floor plan is an available data source in many cities, which have long-standing routines to collect the construction and renovation drawings. The building drawings can be requested online at low prices, such as in Hong Kong’s BRAVO
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