Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds

Ai, Mengchi; Li, Zhixin; Shan, Jie

doi:10.3390/rs13193844

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…They used three levels of constraints, semantic, geometric, and topological, which were introduced to the process of room segmentation for their easier recognition from the point cloud. Similarly, the topological consistency of the generated model was the topic presented by Ai et al [60]. The first specific model subtype, the mesh model, was developed in the works of Turner et al [17] and Turner and Zakhor [18].…”

Section: D Model Reconstructionmentioning

confidence: 97%

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Pokuciński,

Mrozek

2024

Applied Sciences

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The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods.

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Section: D Model Reconstructionmentioning

confidence: 97%

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Pokuciński,

Mrozek

2024

Applied Sciences

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“…Topological structure of rooms is of great significance for building information models (BIMs). Ai et al [19] propose a method for reconstructing indoor models based on the spatial relationships between internal structures, but it may lose many finer details. Considering that many buildings are designed with cube-shaped structures, Wei et al [20] and Li et al [7] use the Manhattan hypothesis to extract the simplified models of the building from point clouds.…”

Section: Geometric Primitive-based Modelingmentioning

confidence: 99%

Hybrid 3D Reconstruction of Indoor Scenes Integrating Object Recognition

Li,

et al. 2024

Remote Sensing

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Indoor 3D reconstruction is particularly challenging due to complex scene structures involving object occlusion and overlap. This paper presents a hybrid indoor reconstruction method that segments the room point cloud into internal and external components, and then reconstructs the room shape and the indoor objects in different ways. We segment the room point cloud into internal and external points based on the assumption that the room shapes are composed of some large external planar structures. For the external, we seek for an appropriate combination of intersecting faces to obtain a lightweight polygonal surface model. For the internal, we define a set of features extracted from the internal points and train a classification model based on random forests to recognize and separate indoor objects. Then, the corresponding computer aided design (CAD) models are placed in the target positions of the indoor objects, converting the reconstruction into a model fitting problem. Finally, the indoor objects and room shapes are combined to generate a complete 3D indoor model. The effectiveness of this method is evaluated on point clouds from different indoor scenes with an average fitting error of about 0.11 m, and the performance is validated by extensive comparisons with state-of-the-art methods.

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“…Research and applications on building modeling, including the classification of building scenes and the extraction of building structure elements, are currently well-developed. Ai et al [ 17 ] proposed a modeling method based on consistent topological rules, which preprocesses semantic information to obtain evenly structured patches and estimates boundaries using an edge-based primitive extraction method. The model is finally reconstructed to achieve building structure modeling in complex environments.…”

Section: Related Workmentioning

confidence: 99%

Generating Topologically Consistent BIM Models of Utility Tunnels from Point Clouds

Yang

Zhang

Yang

et al. 2023

Sensors

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The development and utilization of urban underground space is an important way to solve the “great urban disease”. As one of the most important types of urban underground foundations, utility tunnels have become increasingly popular in municipal construction. The investigation of utility tunnels is a general task and three-dimensional laser scanning technology has played a significant role in surveying and data acquisition. However, three-dimensional laser scanning technology suffers from noise and occlusion in narrow congested utility tunnel spaces, and the acquired point clouds are imperfect; hence, errors and redundancies are introduced in the extracted geometric elements. The topology of reconstructed BIM objects cannot be ensured. Therefore, in this study, a hierarchical segmentation method for point clouds and a topology reconstruction method for building information model (BIM) objects in utility tunnels are proposed. The point cloud is segmented into facades, planes, and pipelines hierarchically. An improved mean-shift algorithm is proposed to extract wall line features and a local symmetry-based medial axis extraction algorithm is proposed to extract pipelines from point clouds. A topology reconstruction method that searches for the neighbor information of wall and pipeline centerlines and establishes collinear, perpendicular, and intersecting situations is used to reconstruct a topologically consistent 3D model of a utility tunnel. An experiment on the Guangzhou’s Nansha District dataset successfully reconstructed 24 BIM wall objects and 12 pipelines within the utility tunnel, verifying the efficiency of the method.

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Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds

Cited by 7 publications

References 47 publications

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

Hybrid 3D Reconstruction of Indoor Scenes Integrating Object Recognition

Generating Topologically Consistent BIM Models of Utility Tunnels from Point Clouds

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