In this paper, a new method capable to extract the wall openings (windows and doors) of interior scenes from point clouds under cluttered and occluded environments is presented. For each wall surface extracted by the polyhedral model of a room, our method constructs a cell complex representation, which is used for the wall object segmentation using a graph cut method. We evaluate the results of the proposed approach on real-world 3D scans of indoor environments and demonstrate its validity. Bayesian Graph-Cut Optimization for Wall Surfaces Reconstruction in Indoor EnvironmentsGeorgios-Tsampikos Michailidis · Renato PajarolaReceived: date / Accepted: date Abstract In this paper, a new method capable to extract the wall openings (windows and doors) of interior scenes from point clouds under cluttered and occluded environments is presented. For each wall surface extracted by the polyhedral model of a room, our method constructs a cell complex representation, which is used for the wall object segmentation using a graph cut method. We evaluate the results of the proposed approach on real-world 3D scans of indoor environments and demonstrate its validity.

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The recent advances in 3D laser range scanning have led to significant improvements in capturing and modeling 3D environments , allowing the creation of highly expressive and semantically rich 3D models from indoor environments, generally known as building information models. Despite the capabilities of state-of-the-art methods to generate faithful architectural 3D building models, the majority of them rely explicitly on the prior knowledge of scanner positions in order to reconstruct them successfully. However, in real-world applications, this metadata information gets typically lost after the point cloud registration, which means that none of these methods could work in practice and the creation of their building models would be impossible. Therefore, we present a novel pipeline that allows to automatically and accurately reconstruct the original scanner positions under very challenging conditions, without requiring any prior knowledge about the environment or the dataset. Being independent from laser range scanner manufacturers, it can be applied to almost every real-world LiDAR application. Our method exploits only information derived from the raw point data and is applicable to all scientific and industrial applications, where the original scan positions typically get lost after registration by the proprietary software provided by the scanner manufacturers. We demonstrate the validity of our approach by evaluating it on several real-world and synthetic indoor environments.

In computer vision, the extraction of dense and accurate disparity maps is a computationally expensive and challenging problem, and high quality results typically require from several seconds to several minutes to be obtained. In this paper, we present a new post-processing technique, which detects the incorrect reconstructed pixels after the initial matching process and replaces them with correct disparity values. Experimental results with Middlebury data sets show that our approach can process images of up to 3MPixels in less than 3.3 msec, producing at the same time semi-dense (up to 99%) and accurate (up to 94%) disparity maps. We also propose a way to adaptively change, in real time, the density and the accuracy of the extracted disparity maps. In addition, the matching and post-processing procedures are calculated without using any multiplication, which makes the algorithm very fast, while its reduced complexity simplifies its implementation. Finally, we present the hardware implementation of the proposed algorithm.

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