Abstract
Recent developments in laser scanning systems have inspired substantial interest in indoor modeling. Semantically rich indoor models are required in many fields. Despite the rapid development of 3D indoor reconstruction methods for building interiors from point clouds, the indoor reconstruction of multi-room environments with curved walls is still not resolved. This study proposed a novel straight and curved line tracking method followed by a straight line test. Robust parameters are used, and a novel straight line regularization method is achieved using constrained least squares. The method constructs a cell complex with both straight lines and curved lines, and the indoor reconstruction is transformed into a labeling problem that is solved based on a novel Markov Random Field formulation. The optimal labeling is found by minimizing an energy function by applying a minimum graph cut approach. Detailed experiments were conducted, and the results indicate that the proposed method is well suited for 3D indoor modeling in multi-room indoor environments with curved walls.
Highlights
3D building modeling has been increasingly requested for a variety of applications, such as building information models (BIM) [1], indoor navigation and positioning, emergency services, smart building, and architectural planning and simulations
computer-aided design (CAD) models focus on representing geometry digitally for the purpose of design and simulation
To the best of our knowledge, no previous research has attempted to solve the problem of indoor reconstruction from point clouds in multi-room indoor environments with curved walls
Summary
3D building modeling has been increasingly requested for a variety of applications, such as building information models (BIM) [1], indoor navigation and positioning, emergency services, smart building, and architectural planning and simulations. Advances in laser scanning systems have provided an efficient way to collect 3D point clouds of both indoor and outdoor scenes This procedure is challenging due to complex building layouts and the high presence of objects such as furniture and wall hangings that cause clutter and occlusions [4]. Recent works have exploited prior knowledge about building structures to achieve robustness and transform scene reconstruction into an indoor space decomposition problem. Sensors 2019, 19, 3798 methods for building interiors from point clouds, the reconstruction of curved walls in multi-room environments is still not resolved. Room space decomposition with curved walls is cast as an energy minimization problem that partitions the indoor space into separate semantic entities.
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