Modelling of Indoor Environments Using Lindenmayer Systems

Peter, Michael

doi:10.5194/isprs-archives-xlii-2-w7-385-2017

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Indoor grammar-based Reconstruction: One popular modeling approach, especially in regular environments, is adopting a (shape) grammar [27][28][29], Lindenmayer Systems (L-systems) [30] or (inverse) procedural modeling [31-34] approaches for interiors. Becker et al…”

Section: Related Workmentioning

confidence: 99%

“…Their approach cannot model rounded walls because their hypothesis is based on extracting rectangles. Mura et al [10] apply the piecewise-planar detection and encode the adjacency of planar segments into a graph that represents the scene.Indoor grammar-based Reconstruction: One popular modeling approach, especially in regular environments, is adopting a (shape) grammar [27][28][29], Lindenmayer Systems (L-systems) [30] or (inverse) procedural modeling [31-34] approaches for interiors. Becker et al[5] use a combination of split grammar and L-system to reconstruct a 3D model for as-built BIM (Building Information Model).…”

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confidence: 99%

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Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

Nikoohemat

Peter²,

Elberink

et al. 2018

Remote Sensing

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The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes. the complexity of 3D space. Few works deal with arbitrary wall layouts [6][7][8], but they are restricted to vertical walls and horizontal ceilings. Our method detects slanted walls and sloped ceilings exploiting the adjacency of permanent structures, based on the assumption that there is less clutter near the ceiling in indoor environments. Additionally, the arbitrary arrangements of walls (non-Manhattan-World) will be handled in this work. Our pipeline for semantic labeling of permanent structure uses detection of planar primitives labelled as wall, floor and ceiling, and their topological relations.Room segmentation is another research problem in large-scale indoor modeling. In the literature, different approaches, such as Voronoi graphs, cell decomposition, binary space partitioning and morphology operators [9] are suggested for 2D and 3D room segmentation. Some of these methods have limitations, such as Manhattan-World constraints and vertical walls. Most of the room segmentation methods rely on the viewpoint [8,10] and require scanning with a TLS in each room [7]. However, as opposed to one scanning location per room, mobile laser scanning systems produce a continuous trajectory and assigning points per room based on the scan location is not possible. Similar to our method for trajectory analysis, refs. [11,12] exploit the trajectory for space subdivision. Although their focus is only on space subdivision and simple structure, their results support our motivation of using the trajectory for interpretation of point clouds.In our pipeline, a novel method is suggested for partitioning interior spaces based on voxels and exploiting unoccupied space. Besides knowing the room layout, information about the doors, walkable space and stairs supports navigation planning. Therefore, voxels are used to identify the walkable space and the trajectory to identify the stairs and doors.Reflective surfaces, such as glass, complicate the analysis of indoor point clouds. Such surfaces cause the appearance of "ghost walls" in the...

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

Nikoohemat

Peter²,

Elberink

et al. 2018

Remote Sensing

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“…Indoor grammar-based Reconstruction: One popular modeling approach, especially in regular environments, is adopting a (shape) grammar (Gips and Stiny, 1980;Stiny, 1982;Tran et al, 2017), Lindenmayer Systems (Lsystems) (Peter, 2017) or (inverse) procedural modeling (Bokeloh et al, 2010;Martinovic and Van Gool, 2013;Müller et al, 2006;Wonka et al, 2003) approaches for interiors. Becker et al (Becker et al, 2015) use a combination of split grammar and L-system to reconstruct a 3D model for as-built BIM (Building Information Model).…”

Section: Data Acquisitionmentioning

confidence: 99%

Indoor 3D modeling of buildings from point clouds

Nikoohemat¹

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When I was writing this acknowledgement, all the last four years flashed back to me, the efforts, deadlines, conferences, stress, laughs and parties. It was a trajectory not only of research but also of experiences, how to cope with challenging situations, and keeping the life-work balance! Traveling my PhD trajectory was not possible without many colleagues and friends whom I would like to thank here. I would like to thank my supervisor, George who taught me how to be precise at my work and stay with what I plan, I tried to learn it from you George. He is the teacher who is meticulous both in administration and research. His knowledge of Lidar and photogrammetry was very valuable in my research. Thanks to my former co-supervisor, Michael Peter, he helped me like a friend in the early stage of my PhD and instilled me what to do when I was lost. Without his support and enthusiasm, I could not get the best paper award. Some ideas in this PhD work was initialized by Sander, thank you for your concise and effective input. Abdoulaye, you are the funniest and smartest buddy. Although most of our collaboration was from a far distance (Delft -Sydney -Enschede), we made it to the end of the project with success. Without the discussions, conference calls and brainstorming, this work was not possible (of course, it was ;)). Sisi, you are the kindest teacher for me and many others. Thank you for giving me the chance to work at your group at GRID, UNSW, Sydney.Thanks to my friends and colleagues at ITC. To all of you, because of the small chats at the coffee machine, ITC lunchtime, international events and food festivals, which all helped me to speak out my thoughts and relieve my stress. I would like to thank especially two persons at ITC: Roelof because of his smile every morning, which gave me energy for the rest of the day, and to Teresa, our EOS office manager who carefully organized yearly events, staff presents and many more. To my office-mates Diogo and ZhenChao for all the laughter and talks about research and life. To my neighbor's office, Parya for the small chats and cookies, you saved me from starving. To Fashuai and Mila for their support and friendship, traveling in China was one of my best research travels with you two. Thanks to my Persian friends for the Persian parties and food. To all EOS Friends Group for the best moments which we spent together, it was four years full of memories. Thank you my paranymphs, Sebastian and Caroline for supporting me until the last moment of my defense.x This work was not possible without the input of the users and partners in SIMs3D project. Kourosh and Sisi for the initiation of the project, the user committee and Dutch Fire-brigade members, especially Huib Fransen and Gerke Spaling for providing scanning sites for use cases, Robert from CGI and Bart from Cyclomedia for their feedback, and Markus Gerke and his team from TU Braunschweig for scanning of several buildings. I would like to appreciate Liangliang and Jantien from TU Delft for taking the responsibility of leading ...

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Room Shapes and Functional Uses Predicted From Sparse Data

Dehbi

Gojayeva

Pickert

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Many researchers used expensive 3D laser scanning techniques to derive indoor models. Few papers describe the derivation of indoor models based on sparse data such as footprints. They assume that floorplans and rooms are rather rectangular and that information on functional use is given. This paper addresses the automatic learning of a classifier which predicts the functional use of housing rooms. The classification is based on features which are widely available such as room areas and orientation. These features are extracted from an extensive database of annotated rooms. A Bayesian classifier is applied which delivers probabilities of competing class hypotheses. In a second step, functional uses are used to predict the shape of the rooms in a further classification.</p>

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Modelling of Indoor Environments Using Lindenmayer Systems

Cited by 3 publications

References 21 publications

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

Indoor 3D modeling of buildings from point clouds

Room Shapes and Functional Uses Predicted From Sparse Data

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