Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds

Cui, Yang; Li, Qingquan; Wang, Jinhu; Xiao, Wen; Chen, Chi

doi:10.1109/jstars.2019.2918937

Cited by 60 publications

(40 citation statements)

References 51 publications

(84 reference statements)

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“…Oesau et al [62] projected wall points horizontally, detected line segments using multiscale RANSAC, and obtained the indoor structures of a building by analyzing the intersections between line segments. Similar procedures for line primitive-based reconstruction were also used in [63]- [65]. Yang et al [66] projected wall points in MLS point clouds on the ground and then detected and connected linear primitives to construct 2-D building footprints.…”

Section: ) Object Extractionmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platform, e.g., mobile laser. However, in this article, lidar point clouds are understood from a new and universal perspective, i.e., geometric primitives embedded in versatile objects in the physical world. In lidar point clouds, the basic unit is the point coordinate. Geometric primitives that consist of a group of discrete points may be viewed as one kind of abstraction and representation of lidar data at the entity level. We categorize geometric primitives into two classes: shape primitives, e.g., lines, surfaces, and volumetric shapes, and structure primitives, represented by skeletons and edges. In recent years, many efforts from different communities, such as photogrammetry, computer vision, and computer graphics, have been made to finalize geometric primitive detection, regularization, and in-depth applications. Interpretations of geometric primitives from multiple disciplines try to convey the significance of geometric primitives, the latest processing techniques regarding geometric primitives, and their potential possibilities in the context of lidar point clouds. To this end, primitive-based applications are reviewed with an emphasis on object extraction and reconstruction to clearly show the significances of this article. Next, we survey and compare methods for geometric primitive extraction and then review primitive regularization methods that add real-world constrains to initial primitives. Finally, we summarize the challenges, expected applications, and describe possible future for primitive extraction methods that can achieve globally optimal results efficiently, even with disorganized, uneven, noisy, incomplete, and large-scale lidar point clouds.

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Section: ) Object Extractionmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Xiong and Huber [43] used a conditional random field model to discover contextual information to aid the construction of a 3D semantic model. Some machine learning methods have been proposed for learning unique features of different structure types and labeling structures [44,45].…”

Section: (B) Ssmmentioning

confidence: 99%

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Shi

Zeng

2020

IJGI

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Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.

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“…Manual construction of simulation geometries based on floorplans and using the hands-on knowledge about the interior design is tedious and time-consuming task. Therefore, automatic environment reconstruction methods are gaining in popularity [105].…”

Section: Environment Modellingmentioning

confidence: 99%

“…Indoor modelling requires even more accurate feature extraction including the identification of the object composition in order to choose appropriate electrical properties. Structural elements, such as doors, windows, walls, floors and ceilings, can be detected by their shape [105]. Detecting missing information due to occlusion can be partially achieved by the identification of structural elements [108].…”

Section: Environment Modellingmentioning

confidence: 99%

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

Novak

2020

J. commun. softw. syst. (Online)

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In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine.

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Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds

Cited by 60 publications

References 51 publications

Geometric Primitives in LiDAR Point Clouds: A Review

Geometric Primitives in LiDAR Point Clouds: A Review

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

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