Floorplan generation from 3D point clouds: A space partitioning approach

Fang, Hao; Lafarge, Florent; Pan, Cihui; Huang, Hui

doi:10.1016/j.isprsjprs.2021.02.012

Cited by 37 publications

(11 citation statements)

References 40 publications

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“…Planar primitive detection [3,31,32,[36][37][38][39][40]45,47,[50][51][52][53][54][55][56][57][58]] Volumetric primitive fitting [59,60] Mesh-based reconstruction [5,11,30,37,57,58,[61][62][63] Next, an overview of the state of the art on outdoor and indoor modelling will be performed. Table 7 categorizes the target environments addressed in recent literature.…”

Section: Methods Referencesmentioning

confidence: 99%

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Abreu

Pinto

Matos

et al. 2023

IJGI

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Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling.

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Section: Methods Referencesmentioning

confidence: 99%

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Abreu

Pinto

Matos

et al. 2023

IJGI

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“…They then used integer linear programming (ILP) to solve this optimisation problem and produced a space layout with walls. A similar approach was used in other papers, e.g., Ambruş et al [73], Fang et al [74], Han et al [30], and Turner and Zakhor [75]; however, these approaches use RANSAC-based wall detection in 2D or 3D, which is not robust in the presence of noise, clutter, or occlusions. Additionally, this has similar limitations to other methods based on the X-Y projections mentioned above.…”

Section: Layout Reconstructionmentioning

confidence: 99%

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

Drobnyi

Fathy

et al. 2023

Sensors

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Most of the buildings that exist today were built based on 2D drawings. Building information models that represent design-stage product information have become prevalent in the second decade of the 21st century. Still, it will take many decades before such models become the norm for all existing buildings. In the meantime, the building industry lacks the tools to leverage the benefits of digital information management for construction, operation, and renovation. To this end, this paper reviews the state-of-the-art practice and research for constructing (generating) and maintaining (updating) geometric digital twins. This paper also highlights the key limitations preventing current research from being adopted in practice and derives a new geometry-based object class hierarchy that mainly focuses on the geometric properties of building objects, in contrast to widely used existing object categorisations that are mainly function-oriented. We argue that this new class hierarchy can serve as the main building block for prioritising the automation of the most frequently used object classes for geometric digital twin construction and maintenance. We also draw novel insights into the limitations of current methods and uncover further research directions to tackle these problems. Specifically, we believe that adapting deep learning methods can increase the robustness of object detection and segmentation of various types; involving design intents can achieve a high resolution of model construction and maintenance; using images as a complementary input can help to detect transparent and specular objects; and combining synthetic data for algorithm training can overcome the lack of real labelled datasets.

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“…Most researchers utilize lines and planes as primary blocks to build up floor plans. A common way in the literature is to apply RANSAC [7] to fit plane candidates in a point cloud [8] [9] [10], but the computation is expensive and the algorithm is influenced by planar artifacts. Luperto [11] detects line features in a 2D grid map, by combining the Canny edge detection [12] and the directional Hough transform [13].…”

Section: Related Workmentioning

confidence: 99%

Floor-plan generation from noisy point clouds

Liu

Bondarev

With

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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This paper proposes a growing-based floor-plan generation method that creates the global layout of buildings from noisy point clouds obtained by a stereo camera. We introduce a PCA-based line-growing concept with a subsequent filtering step, which is able to robustly handle the high noise levels in input point clouds. Experimental results show that this method outperforms the state-of-the-art techniques in floor-plan generation. The average score for building layouts has increased from 0.38 to 0.66 on our test dataset, compared to the previous best floor-plan generation method. Furthermore, the resulting floor plans are multiple thousands of times smaller in memory size than the input point clouds, while still preserving the main building structures.

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Floorplan generation from 3D point clouds: A space partitioning approach

Cited by 37 publications

References 40 publications

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

Floor-plan generation from noisy point clouds

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