Modeling of Architectural Components for Large-Scale Indoor Spaces From Point Cloud Measurements

Lim, Gahyeon; Oh, Youjin; Kim, Dongwoo; Jun, Changhyun; Kang, Jaehyeon; Doh, Nakju Lett

doi:10.1109/lra.2020.2976327

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…The registered points can be segmented into structural points, non-structural (object) points, and noise points. Structural points are extracted using the architectural point cloud construction approach in [ 16 ]. In Table 3 , the “#Registered points” and “#Structural points” indicate the number of registered point cloud and structural points, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…We acquire the registered point cloud and trajectory, which are the optimized poses of raw LiDAR measurements, using the LiDAR-IMU based simultaneous localization and mapping (SLAM) [ 29 , 30 ]. The structural points are constructed using the architectural point cloud construction described in our previous work [ 16 ]. First, the registered point cloud is segmented into structural and non-structural components.…”

Section: Methodsmentioning

confidence: 99%

“…Nikoohemat et al [ 10 ] presented an adjacency graph based permanent structures detection method. Lim et al [ 16 ] not only segmented structural and object points but also filled in the holes that are used to construct architectural points by projecting object points onto the structural surfaces. Coudron et al [ 23 ] used deep learning to extract permanent structures.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed method is improved over our previous work [ 16 ]. Our previous work used random sample consensus (RANSAC) based plane extraction and constructed an adjacency graph to reconstruct indoor spaces, but this approach requires manual work when generating the adjacency graph.…”

Section: Introductionmentioning

confidence: 99%

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

Lim

Doh

2021

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lim

Doh

2021

Sensors

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“…Recent works overcome Manhattan assumption for indoor modeling or integrate the 3D model with IBR. 9,14 Hedman et al 2 proposed a real-time IBR method. However, this method has limitations in rendering larger than room-scale spaces.…”

Section: Indoor Modeling and Ibrmentioning

confidence: 99%

Generative multiview inpainting for object removal in large indoor spaces

Kim

Hyeon

Doh

2021

International Journal of Advanced Robotic Systems

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As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the colored point clouds onto the image planes. Next, a generative inpainting network accepts a masked image, a color prior image, imperfect guideline, and two different masks as inputs and yields the refined guideline and inpainted image as outputs. The color prior and guideline input ensure color and boundary consistencies across multiple images. We validate our pipeline on real indoor data sets quantitatively using consistency distance and similarity distance, metrics we defined for comparing results of multiview inpainting and qualitatively.

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Spatial template-based geometric complexity reduction method for photo-realistic modeling of large-scale indoor spaces

Hyeon

Kim

Choi³

et al. 2022

Engineering Applications of Artificial Intelligence

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Modeling of Architectural Components for Large-Scale Indoor Spaces From Point Cloud Measurements

Cited by 11 publications

References 26 publications

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

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

Generative multiview inpainting for object removal in large indoor spaces

Spatial template-based geometric complexity reduction method for photo-realistic modeling of large-scale indoor spaces

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