A paper regarding the automatic generation of accurate floor plans and 3D models from scanned mesh data for interior design and navigation.
Authors: Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; Eric Bier, Palo Alto Research Center, USA bier@parc.com; Lester Nelson, Palo Alto Research Center, USA lnelson@parc.
com; Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 5 Conclusion & Future work In summary, our new approach for generating floor plans from triangle mesh data collected by augmented reality headsets produces two styles: a detailed pen-and-ink style and a simplified drafting style. Our algorithms align the mesh data with primary coordinate axes to produce tidy floor plans with vertical and horizontal walls, while also allowing for the removal of ceilings and floors and the separation of multi-story buildings into individual stories. Our approach integrates with AR, supporting the addition of synthetic objects to physical geometry and providing a detailed 3D model and floor plan. Potential applications include navigation, interior design, furniture placement, facility management, building construction, and HVAC design. Moving forward, we plan to enable support for sloping ceilings, automate wall and door detection, and integrate with other tools such as energy simulators. Finally, we plan to compare our approach with existing state-of-the-art methods in terms of accuracy and computational time. We also plan to explore the applicability of block-based DBScan for 3D reconstruction from incomplete scans. Our approach has the potential to revolutionize the way we generate and visualize floor plans. References Adan, A., Huber, D.: 3d reconstruction of interior wall surfaces under occlusion and clutter. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. pp. 275–281 . https://doi.org/10.1109/3DIMPVT.2011.42 Arikan, M., Schwärzler, M., Flöry, S., Wimmer, M., Maierhofer, S.: O-snap: Optimization-based snapping for modeling architecture. ACM Trans. Graph. 32 . https://doi.org/10.1145/2421636.2421642 Budroni, A., Boehm, J.: Automated 3d reconstruction of interiors from point clouds. International Journal of Architectural Computing 8, 55–73 . https://doi.org/10.1260/1478-0771.8.1.55 Cabral, R.S., Furukawa, Y.: Piecewise planar and compact floorplan reconstruction from images. 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 628–635 Cai, R., Li, H., Xie, J., Jin, X.: Accurate floorplan reconstruction using geometric priors. Computers & Graphics 102, 360-369 . https://doi.org/10.1016/j.cag.2021.10.011 Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-sp: Inverse cad for floorplans by sequential room-wise shortest path. In: The IEEE International Conference on Computer Vision Chen, N., Lu, Z., Yu, X., Yang, L., Xu, P., Fan, Y.: Augmented reality-based home interaction layout and evaluation. In: Computer Graphics International Conference. pp. 395–406. Springer Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: Robust spatial layout estimation for cluttered indoor scenes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition . pp. 616–624 . https://doi.org/10.1109/CVPR.2016.73 Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 80–87 . https://doi.org/10.1109/ICCV.2009.5459145 Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., Wang, T., Li, X.: Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. p. 249–260. MobiCom ’14, Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/2639108.2639134 Hsiao, C.W., Sun, C., Sun, M., Chen, H.T.: Flat2layout: Flat representation for estimating layout of general room types. ArXiv abs/1905.12571 Ikehata, S., Yang, H., Furukawa, Y.: Structured indoor modeling. In: 2015 IEEE International Conference on Computer Vision . pp. 1323–1331 . https://doi.org/10.1109/ICCV.2015.156 Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A.: Double refinement network for room layout estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. Pattern Recognition. pp. 557–568. Springer International Publishing, Cham Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: Endto-end room layout estimation. 2017 IEEE International Conference on Computer Vision pp. 4875–4884 Liu, C., Wu, J., Furukawa, Y.: Floornet: A unified framework for floorplan reconstruction from 3d scans. In: ECCV Liu, H., Yang, Y.L., AlHalawani, S., Mitra, N.J.: Constraint-aware interior layout exploration for precast concrete-based buildings. Visual Computer McNeel, R., et al.: Rhinoceros 3d, version 6.0. Robert McNeel & Associates, Seattle, WA Microsoft: Spatial mapping. https://docs.microsoft.com/en-us/windows/mixed-reality/spatial-mapping Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: Rapter: Rebuilding manmade scenes with regular arrangements of planes. ACM Trans. Graph. 34 . https://doi.org/10.1145/2766995 Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multiroom interiors with arbitrary wall arrangements. Computer Graphics Forum 35, 179–188 . https://doi.org/https://doi.org/10.1111/cgf.13015 Murali, S., Speciale, P., Oswald, M.R., Pollefeys, M.: Indoor scan2bim: Building information models of house interiors. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems . pp. 6126–6133 . https://doi. org/10.1109/IROS.2017.8206513 Okorn, B., Xiong, X., Akinci, B.: Toward automated modeling of floor plans. In: In Proceedings of the symposium on 3D data processing, visualization and transmission. vol. 2 Pintore, G., Gobbetti, E.: Effective mobile mapping of multi-room indoor structures. The visual computer 30, 707–716 Pintore, G., Mura, C., Ganovelli, F., Fuentes-Perez, L.J., Pajarola, R., Gobbetti, E.: State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments. Computer Graphics Forum . https://doi.org/10.1111/cgf.14021 Ramakrishnan, S.K., Gokaslan, A., Wijmans, E., Maksymets, O., Clegg, A., Turner, J.M., Undersander, E., Galuba, W., Westbury, A., Chang, A.X., Savva, M., Zhao, Y., Batra, D.: Habitat-matterport 3d dataset : 1000 large-scale 3d environments for embodied AI. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , https://openreview.net/forum?id=-v4OuqNs5P Turner, E., Zakhor, A.: Watertight as-built architectural floor plans generated from laser range data. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission. pp. 316–323 . https: //doi.org/10.1109/3DIMPVT.2012.80 Weinmann, M., Wursthorn, S., Weinmann, M., Hübner, P.: Efficient 3d mapping and modelling of indoor scenes with the microsoft hololens: A survey. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 89, 319–333 Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3d building models from laser scanner data. Automation in Construction 31, 325–337 . https://doi.org/10.1016/j.autcon.2012.10.006 Zhang, J., Kan, C., Schwing, A.G., Urtasun, R.: Estimating the 3d layout of indoor scenes and its clutter from depth sensors. In: 2013 IEEE International Conference on Computer Vision. pp. 1273–1280 . https://doi.org/10.1109/ICCV.2013.161 Zou, C., Colburn, A., Shan, Q., Hoiem, D.: Layoutnet: Reconstructing the 3d room layout from a single rgb image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . pp. 2051–2059. IEEE Computer Society, Los Alamitos, CA, USA . https://doi.org/10.1109/CVPR.2018.00219 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; Eric Bier, Palo Alto Research Center, USA bier@parc.com; Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; Eric Bier, Palo Alto Research Center, USA bier@parc.com; Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References 5 Conclusion & Future work In summary, our new approach for generating floor plans from triangle mesh data collected by augmented reality headsets produces two styles: a detailed pen-and-ink style and a simplified drafting style. Our algorithms align the mesh data with primary coordinate axes to produce tidy floor plans with vertical and horizontal walls, while also allowing for the removal of ceilings and floors and the separation of multi-story buildings into individual stories. Our approach integrates with AR, supporting the addition of synthetic objects to physical geometry and providing a detailed 3D model and floor plan. Potential applications include navigation, interior design, furniture placement, facility management, building construction, and HVAC design. Moving forward, we plan to enable support for sloping ceilings, automate wall and door detection, and integrate with other tools such as energy simulators. Finally, we plan to compare our approach with existing state-of-the-art methods in terms of accuracy and computational time. We also plan to explore the applicability of block-based DBScan for 3D reconstruction from incomplete scans. Our approach has the potential to revolutionize the way we generate and visualize floor plans. References Adan, A., Huber, D.: 3d reconstruction of interior wall surfaces under occlusion and clutter. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. pp. 275–281 . https://doi.org/10.1109/3DIMPVT.2011.42 Arikan, M., Schwärzler, M., Flöry, S., Wimmer, M., Maierhofer, S.: O-snap: Optimization-based snapping for modeling architecture. ACM Trans. Graph. 32 . https://doi.org/10.1145/2421636.2421642 Budroni, A., Boehm, J.: Automated 3d reconstruction of interiors from point clouds. International Journal of Architectural Computing 8, 55–73 . https://doi.org/10.1260/1478-0771.8.1.55 Cabral, R.S., Furukawa, Y.: Piecewise planar and compact floorplan reconstruction from images. 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 628–635 Cai, R., Li, H., Xie, J., Jin, X.: Accurate floorplan reconstruction using geometric priors. Computers & Graphics 102, 360-369 . https://doi.org/10.1016/j.cag.2021.10.011 Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-sp: Inverse cad for floorplans by sequential room-wise shortest path. In: The IEEE International Conference on Computer Vision Chen, N., Lu, Z., Yu, X., Yang, L., Xu, P., Fan, Y.: Augmented reality-based home interaction layout and evaluation. In: Computer Graphics International Conference. pp. 395–406. Springer Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: Robust spatial layout estimation for cluttered indoor scenes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition . pp. 616–624 . https://doi.org/10.1109/CVPR.2016.73 Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 80–87 . https://doi.org/10.1109/ICCV.2009.5459145 Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., Wang, T., Li, X.: Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. p. 249–260. MobiCom ’14, Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/2639108.2639134 Hsiao, C.W., Sun, C., Sun, M., Chen, H.T.: Flat2layout: Flat representation for estimating layout of general room types. ArXiv abs/1905.12571 Ikehata, S., Yang, H., Furukawa, Y.: Structured indoor modeling. In: 2015 IEEE International Conference on Computer Vision . pp. 1323–1331 . https://doi.org/10.1109/ICCV.2015.156 Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A.: Double refinement network for room layout estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. Pattern Recognition. pp. 557–568. Springer International Publishing, Cham Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: Endto-end room layout estimation. 2017 IEEE International Conference on Computer Vision pp. 4875–4884 Liu, C., Wu, J., Furukawa, Y.: Floornet: A unified framework for floorplan reconstruction from 3d scans. In: ECCV Liu, H., Yang, Y.L., AlHalawani, S., Mitra, N.J.: Constraint-aware interior layout exploration for precast concrete-based buildings. Visual Computer McNeel, R., et al.: Rhinoceros 3d, version 6.0. Robert McNeel & Associates, Seattle, WA Microsoft: Spatial mapping. https://docs.microsoft.com/en-us/windows/mixed-reality/spatial-mapping Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: Rapter: Rebuilding manmade scenes with regular arrangements of planes. ACM Trans. Graph. 34 . https://doi.org/10.1145/2766995 Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multiroom interiors with arbitrary wall arrangements. Computer Graphics Forum 35, 179–188 . https://doi.org/https://doi.org/10.1111/cgf.13015 Murali, S., Speciale, P., Oswald, M.R., Pollefeys, M.: Indoor scan2bim: Building information models of house interiors. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems . pp. 6126–6133 . https://doi. org/10.1109/IROS.2017.8206513 Okorn, B., Xiong, X., Akinci, B.: Toward automated modeling of floor plans. In: In Proceedings of the symposium on 3D data processing, visualization and transmission. vol. 2 Pintore, G., Gobbetti, E.: Effective mobile mapping of multi-room indoor structures. The visual computer 30, 707–716 Pintore, G., Mura, C., Ganovelli, F., Fuentes-Perez, L.J., Pajarola, R., Gobbetti, E.: State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments. Computer Graphics Forum . https://doi.org/10.1111/cgf.14021 Ramakrishnan, S.K., Gokaslan, A., Wijmans, E., Maksymets, O., Clegg, A., Turner, J.M., Undersander, E., Galuba, W., Westbury, A., Chang, A.X., Savva, M., Zhao, Y., Batra, D.: Habitat-matterport 3d dataset : 1000 large-scale 3d environments for embodied AI. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , https://openreview.net/forum?id=-v4OuqNs5P Turner, E., Zakhor, A.: Watertight as-built architectural floor plans generated from laser range data. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission. pp. 316–323 . https: //doi.org/10.1109/3DIMPVT.2012.80 Weinmann, M., Wursthorn, S., Weinmann, M., Hübner, P.: Efficient 3d mapping and modelling of indoor scenes with the microsoft hololens: A survey. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 89, 319–333 Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3d building models from laser scanner data. Automation in Construction 31, 325–337 . https://doi.org/10.1016/j.autcon.2012.10.006 Zhang, J., Kan, C., Schwing, A.G., Urtasun, R.: Estimating the 3d layout of indoor scenes and its clutter from depth sensors. In: 2013 IEEE International Conference on Computer Vision. pp. 1273–1280 . https://doi.org/10.1109/ICCV.2013.161 Zou, C., Colburn, A., Shan, Q., Hoiem, D.: Layoutnet: Reconstructing the 3d room layout from a single rgb image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . pp. 2051–2059. IEEE Computer Society, Los Alamitos, CA, USA . https://doi.org/10.1109/CVPR.2018.00219 Adan, A., Huber, D.: 3d reconstruction of interior wall surfaces under occlusion and clutter. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. pp. 275–281 . https://doi.org/10.1109/3DIMPVT.2011.42 Adan, A., Huber, D.: 3d reconstruction of interior wall surfaces under occlusion and clutter. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. pp. 275–281 . https://doi.org/10.1109/3DIMPVT.2011.42 https://doi.org/10.1109/3DIMPVT.2011.42 Arikan, M., Schwärzler, M., Flöry, S., Wimmer, M., Maierhofer, S.: O-snap: Optimization-based snapping for modeling architecture. ACM Trans. Graph. 32 . https://doi.org/10.1145/2421636.2421642 Arikan, M., Schwärzler, M., Flöry, S., Wimmer, M., Maierhofer, S.: O-snap: Optimization-based snapping for modeling architecture. ACM Trans. Graph. 32 . https://doi.org/10.1145/2421636.2421642 https://doi.org/10.1145/2421636.2421642 Budroni, A., Boehm, J.: Automated 3d reconstruction of interiors from point clouds. International Journal of Architectural Computing 8, 55–73 . https://doi.org/10.1260/1478-0771.8.1.55 Budroni, A., Boehm, J.: Automated 3d reconstruction of interiors from point clouds. International Journal of Architectural Computing 8, 55–73 . https://doi.org/10.1260/1478-0771.8.1.55 https://doi.org/10.1260/1478-0771.8.1.55 Cabral, R.S., Furukawa, Y.: Piecewise planar and compact floorplan reconstruction from images. 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 628–635 Cabral, R.S., Furukawa, Y.: Piecewise planar and compact floorplan reconstruction from images. 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 628–635 Cai, R., Li, H., Xie, J., Jin, X.: Accurate floorplan reconstruction using geometric priors. Computers & Graphics 102, 360-369 . https://doi.org/10.1016/j.cag.2021.10.011 Cai, R., Li, H., Xie, J., Jin, X.: Accurate floorplan reconstruction using geometric priors. Computers & Graphics 102, 360-369 . https://doi.org/10.1016/j.cag.2021.10.011 https://doi.org/10.1016/j.cag.2021.10.011 Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-sp: Inverse cad for floorplans by sequential room-wise shortest path. In: The IEEE International Conference on Computer Vision Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-sp: Inverse cad for floorplans by sequential room-wise shortest path. In: The IEEE International Conference on Computer Vision Chen, N., Lu, Z., Yu, X., Yang, L., Xu, P., Fan, Y.: Augmented reality-based home interaction layout and evaluation. In: Computer Graphics International Conference. pp. 395–406. Springer Chen, N., Lu, Z., Yu, X., Yang, L., Xu, P., Fan, Y.: Augmented reality-based home interaction layout and evaluation. In: Computer Graphics International Conference. pp. 395–406. Springer Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: Robust spatial layout estimation for cluttered indoor scenes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition . pp. 616–624 . https://doi.org/10.1109/CVPR.2016.73 Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: Robust spatial layout estimation for cluttered indoor scenes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition . pp. 616–624 . https://doi.org/10.1109/CVPR.2016.73 https://doi.org/10.1109/CVPR.2016.73 Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 80–87 . https://doi.org/10.1109/ICCV.2009.5459145 Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 80–87 . https://doi.org/10.1109/ICCV.2009.5459145 https://doi.org/10.1109/ICCV.2009.5459145 Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., Wang, T., Li, X.: Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. p. 249–260. MobiCom ’14, Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/2639108.2639134 Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., Wang, T., Li, X.: Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. p. 249–260. MobiCom ’14, Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/2639108.2639134 https://doi.org/10.1145/2639108.2639134 Hsiao, C.W., Sun, C., Sun, M., Chen, H.T.: Flat2layout: Flat representation for estimating layout of general room types. ArXiv abs/1905.12571 Hsiao, C.W., Sun, C., Sun, M., Chen, H.T.: Flat2layout: Flat representation for estimating layout of general room types. ArXiv abs/1905.12571 Ikehata, S., Yang, H., Furukawa, Y.: Structured indoor modeling. In: 2015 IEEE International Conference on Computer Vision . pp. 1323–1331 . https://doi.org/10.1109/ICCV.2015.156 Ikehata, S., Yang, H., Furukawa, Y.: Structured indoor modeling. In: 2015 IEEE International Conference on Computer Vision . pp. 1323–1331 . https://doi.org/10.1109/ICCV.2015.156 https://doi.org/10.1109/ICCV.2015.156 Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A.: Double refinement network for room layout estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. Pattern Recognition. pp. 557–568. Springer International Publishing, Cham Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A.: Double refinement network for room layout estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. Pattern Recognition. pp. 557–568. Springer International Publishing, Cham Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: Endto-end room layout estimation. 2017 IEEE International Conference on Computer Vision pp. 4875–4884 Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: Endto-end room layout estimation. 2017 IEEE International Conference on Computer Vision pp. 4875–4884 Liu, C., Wu, J., Furukawa, Y.: Floornet: A unified framework for floorplan reconstruction from 3d scans. In: ECCV Liu, C., Wu, J., Furukawa, Y.: Floornet: A unified framework for floorplan reconstruction from 3d scans. In: ECCV Liu, H., Yang, Y.L., AlHalawani, S., Mitra, N.J.: Constraint-aware interior layout exploration for precast concrete-based buildings. Visual Computer Liu, H., Yang, Y.L., AlHalawani, S., Mitra, N.J.: Constraint-aware interior layout exploration for precast concrete-based buildings. Visual Computer McNeel, R., et al.: Rhinoceros 3d, version 6.0. Robert McNeel & Associates, Seattle, WA McNeel, R., et al.: Rhinoceros 3d, version 6.0. Robert McNeel & Associates, Seattle, WA Microsoft: Spatial mapping. https://docs.microsoft.com/en-us/windows/mixed-reality/spatial-mapping Microsoft: Spatial mapping. https://docs.microsoft.com/en-us/windows/ mixed-reality/spatial-mapping https://docs.microsoft.com/en-us/windows/ Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: Rapter: Rebuilding manmade scenes with regular arrangements of planes. ACM Trans. Graph. 34 . https://doi.org/10.1145/2766995 Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: Rapter: Rebuilding manmade scenes with regular arrangements of planes. ACM Trans. Graph. 34 . https://doi.org/10.1145/2766995 https://doi.org/10.1145/2766995 Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multiroom interiors with arbitrary wall arrangements. Computer Graphics Forum 35, 179–188 . https://doi.org/https://doi.org/10.1111/cgf.13015 Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multiroom interiors with arbitrary wall arrangements. Computer Graphics Forum 35, 179–188 . https://doi.org/https://doi.org/10.1111/cgf.13015 https://doi.org/https://doi.org/10.1111/cgf.13015 Murali, S., Speciale, P., Oswald, M.R., Pollefeys, M.: Indoor scan2bim: Building information models of house interiors. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems . pp. 6126–6133 . https://doi. org/10.1109/IROS.2017.8206513 Murali, S., Speciale, P., Oswald, M.R., Pollefeys, M.: Indoor scan2bim: Building information models of house interiors. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems . pp. 6126–6133 . https://doi . org/10.1109/IROS.2017.8206513 https://doi Okorn, B., Xiong, X., Akinci, B.: Toward automated modeling of floor plans. In: In Proceedings of the symposium on 3D data processing, visualization and transmission. vol. 2 Okorn, B., Xiong, X., Akinci, B.: Toward automated modeling of floor plans. In: In Proceedings of the symposium on 3D data processing, visualization and transmission. vol. 2 Pintore, G., Gobbetti, E.: Effective mobile mapping of multi-room indoor structures. The visual computer 30, 707–716 Pintore, G., Gobbetti, E.: Effective mobile mapping of multi-room indoor structures. The visual computer 30, 707–716 Pintore, G., Mura, C., Ganovelli, F., Fuentes-Perez, L.J., Pajarola, R., Gobbetti, E.: State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments. Computer Graphics Forum . https://doi.org/10.1111/cgf.14021 Pintore, G., Mura, C., Ganovelli, F., Fuentes-Perez, L.J., Pajarola, R., Gobbetti, E.: State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments. Computer Graphics Forum . https://doi.org/10.1111/cgf.14021 https://doi.org/10.1111/cgf.14021 Ramakrishnan, S.K., Gokaslan, A., Wijmans, E., Maksymets, O., Clegg, A., Turner, J.M., Undersander, E., Galuba, W., Westbury, A., Chang, A.X., Savva, M., Zhao, Y., Batra, D.: Habitat-matterport 3d dataset : 1000 large-scale 3d environments for embodied AI. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , https://openreview.net/forum?id=-v4OuqNs5P Ramakrishnan, S.K., Gokaslan, A., Wijmans, E., Maksymets, O., Clegg, A., Turner, J.M., Undersander, E., Galuba, W., Westbury, A., Chang, A.X., Savva, M., Zhao, Y., Batra, D.: Habitat-matterport 3d dataset : 1000 large-scale 3d environments for embodied AI. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , https://openreview.net/forum?id=-v4OuqNs5P https://openreview.net/forum?id=-v4OuqNs5P Turner, E., Zakhor, A.: Watertight as-built architectural floor plans generated from laser range data. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission. pp. 316–323 . https: //doi.org/10.1109/3DIMPVT.2012.80 Turner, E., Zakhor, A.: Watertight as-built architectural floor plans generated from laser range data. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission. pp. 316–323 . https: //doi.org/10.1109/3DIMPVT.2012.80 //doi.org/10.1109/3DIMPVT.2012.80 Weinmann, M., Wursthorn, S., Weinmann, M., Hübner, P.: Efficient 3d mapping and modelling of indoor scenes with the microsoft hololens: A survey. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 89, 319–333 Weinmann, M., Wursthorn, S., Weinmann, M., Hübner, P.: Efficient 3d mapping and modelling of indoor scenes with the microsoft hololens: A survey. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 89, 319–333 Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3d building models from laser scanner data. Automation in Construction 31, 325–337 . https://doi.org/10.1016/j.autcon.2012.10.006 Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3d building models from laser scanner data. Automation in Construction 31, 325–337 . https://doi.org/10.1016/j.autcon.2012.10.006 https://doi.org/10.1016/j.autcon.2012.10.006 Zhang, J., Kan, C., Schwing, A.G., Urtasun, R.: Estimating the 3d layout of indoor scenes and its clutter from depth sensors. In: 2013 IEEE International Conference on Computer Vision. pp. 1273–1280 . https://doi.org/10.1109/ICCV.2013.161 Zhang, J., Kan, C., Schwing, A.G., Urtasun, R.: Estimating the 3d layout of indoor scenes and its clutter from depth sensors. In: 2013 IEEE International Conference on Computer Vision. pp. 1273–1280 . https://doi.org/10.1109/ICCV.2013.161 https://doi.org/10.1109/ICCV.2013.161 Zou, C., Colburn, A., Shan, Q., Hoiem, D.: Layoutnet: Reconstructing the 3d room layout from a single rgb image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . pp. 2051–2059. IEEE Computer Society, Los Alamitos, CA, USA . https://doi.org/10.1109/CVPR.2018.00219 Zou, C., Colburn, A., Shan, Q., Hoiem, D.: Layoutnet: Reconstructing the 3d room layout from a single rgb image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . pp. 2051–2059. IEEE Computer Society, Los Alamitos, CA, USA . https://doi.org/10.1109/CVPR.2018.00219 https://doi.org/10.1109/CVPR.2018.00219 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv
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