Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Abstract

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

Three-Dimensional Linear Restoration of a Tunnel Based on Measured Track and Uncontrolled Mobile Laser Scanning

“…In the case of no design data or inaccurate original design data, the simulation design data can be used to calculate the track regularity. After improving the accuracy, this method may also be used for 3D reconstruction of tunnel scenes [ 32 , 33 ]. At present, the methods of 3D scene reconstruction using the collected 3D point cloud data are mainly based on feature point registration or GNSS/IMU integrated navigation to obtain the attitude and position.…”

Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

“…Automated reconstruction of building environments from indoor mapping data such as point clouds is a wide and active field of research (Kang et al, 2020;Pintore et al, 2020). The various proposed approaches differ significantly in the amount of assumptions that are made with respect to the building structure to be reconstructed and thus in their flexibility towards challenging building environments, ranging from single room scenarios (Li et al, 2020;Sanchez et al, 2020b), Manhattan World structures where all surfaces are orthogonal to the coordinate axes (Ryu et al, 2020;Kim et al, 2020) to diagonal (Shi et al, 2019;Tran and Khoshelham, 2020) or even curved walls (Yang et al, 2019;Wu et al, 2020) and slanted ceilings (Nikoohemat et al, 2020;Lim and Doh, 2021).…”

“…Storey-wise 2D cell complexes (Li et al, 2018;Tran and Khoshelham, 2020) as well as fully threedimensional cell complexes can be used (Coudron et al, 2018;. Other reconstruction methods make use of trajectory information of the mobile mapping system if available (Cui et al, 2019;Nikoohemat et al, 2020;Lim and Doh, 2021) or operate in a discretized voxel grid (Fichtner et al, 2017;Flikweert et al, 2019;Gorte et al, 2019). Recently, reconstruction methods relying on deep learning methods are gaining in prevalence (Kim et al, 2020;Gankhuyag and Han, 2021;Yang et al, 2021).…”

Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds

“…Structural reconstruction in complex environments poses specific challenges due to their complicated spatial relationships, irregular shapes, as well as the existence of occlusion in raw data. Existing solutions can be divided into two general categories: decompositionbased methods and primitives fitting methods [11][12][13]. The decomposition-based methods are designed based on strong assumptions, such as Manhattan-world or 2.5D.…”