Abstract
Large amounts of geographical and property rights information are contained in 3D cadastral property units, although the amount of cadastral information expressed from various perspectives varies. Therefore, it is crucial to choose a perspective that is compatible with human visual perception and conveys the most data as the perspective changes. This paper proposes an optimization method for generating the optimal view of 3D cadastral property units to address the above issues. The construction of candidate perspectives is the first step, followed by the calculation of feature values, including visibility of 3D boundary points under each candidate perspective, visible area ratio, and visual comfort. Then, an improved particle swarm optimization technique is used to determine the optimal view of 3D cadastral property units and the ideal viewpoint for the expected input model. The research results show that the selected optimal viewpoint fully considers human visual comfort, can accommodate a large amount of cadastral information, and can be used as a map** drawing in social applications of 3D cadastral systems. This viewpoint can effectively supplement front views, side views, and axis side views, enabling more visual information to be transmitted in 3D religion maps and 3D property right maps and improving readability.
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The data and code supporting the findings are available in ‘figshare.com’ with the public link https://doi.org/10.6084/m9.figshare.19513693.v1.
References
Bakshi A, Roy S, Mallick A, Ghosh K (2022) A discrete magno–parvo additive model in early vision for explaining brightness perception in varying contrastive contexts. Biol Cybern 116(1):5–21. https://doi.org/10.1007/s00422-021-00896-4
Barton JJS (2021) Motion perception and its disorders. In: Handbook of clinical neurology, vol 178, pp 257–275
Barzegar M, Rajabifard A, Kalantari M, Atazadeh B (2020) 3D BIM-enabled spatial query for retrieving property boundaries: a case study in Victoria, Australia. Int J Geogr Inf Sci 34(2):251–271. https://doi.org/10.1080/13658816.2019.1658877
Blanz V, Tarr MJ, Bülthoff HH (1999) What object attributes determine canonical views? Perception 28(5):575–597. https://doi.org/10.1068/p2897
Dimitrov H, Petrova-Antonova D (2021) 3D city model as a first step towards digital twin of Sofia City. Paper presented at the international archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archives
Dutagaci H, Cheung CP, Godil A (2010) A benchmark for best view selection of 3D objects. Paper presented at the proceedings of the ACM workshop on 3D object retrieval
Erdogan G, Jacobs RA (2017) Visual shape perception as Bayesian inference of 3D object-centered shape representations. Psychol Rev 124(6):740–761. https://doi.org/10.1037/rev0000086
Gkeli M, Potsiou C, Ioannidis C (2020) A technical solution for 3D crowdsourced cadastral surveys. Land Use Policy. https://doi.org/10.1016/j.landusepol.2019.104419
Hairuddin FI, Abdul Rasam AR, Razali MH (2022) Development of a 3d cadastre augmented reality and visualization in malaysia. Paper presented at the international archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archives
Ho S, Rajabifard A (2016) Towards 3D-enabled urban land administration: strategic lessons from the bim initiative in singapore. Land Use Policy 57:1–10. https://doi.org/10.1016/j.landusepol.2016.05.011
Kennedy J, Eberhart R (1995) Particle swarm optimization. Paper presented at the proceedings of ICNN'95—international conference on neural networks
Krukar J, Manivannan C, Bhatt M, Schultz C (2021) Embodied 3D isovists: a method to model the visual perception of space. Environ Plan B Urban Anal City Sci 48(8):2307–2325. https://doi.org/10.1177/2399808320974533
Larsson K, Paasch JM, Paulsson J (2020) Representation of 3D cadastral boundaries—from analogue to digital. Land Use Policy. https://doi.org/10.1016/j.landusepol.2019.104178
Leifman G, Shtrom E, Tal A (2016) Surface regions of interest for viewpoint selection. IEEE Trans Pattern Anal Mach Intell 38(12):2544–2556. https://doi.org/10.1109/tpami.2016.2522437
Liu YJ, Luo X, Joneja A, Ma CX, Fu XL, Song D (2013) User-adaptive sketch-based 3-D CAD model retrieval. IEEE Trans Autom Sci Eng 10(3):783–795. https://doi.org/10.1109/TASE.2012.2228481
Liu R, Li H, Lv Z (2023) Modeling methods of 3D model in digital twins. Comput Model Eng Sci 136(2):985–1022. https://doi.org/10.32604/cmes.2023.023154
Mortara M, Spagnuolo M (2009) Semantics-driven best view of 3D shapes. Comput Graph (pergamon) 33(3):280–290. https://doi.org/10.1016/j.cag.2009.03.003
Neuville R, Pouliot J, Billen R (2019) Identification of the best 3D viewpoint within the BIM model: application to visual tasks related to facility management. Buildings 9(7):167. https://doi.org/10.3390/buildings9070167
Neuville R, Jacynthe P, Billen R (2020) 3D viewpoint optimization of topological relationships: application to 3D cadastre for visual easement validation. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 6:135–142. https://doi.org/10.5194/isprs-annals-vi-4-w1-2020-135-2020
Rosinol A, Violette A, Abate M, Hughes N, Chang Y, Shi J, Carlone L (2021) Kimera: from SLAM to spatial perception with 3D dynamic scene graphs. Int J Robot Res 40(12–14):1510–1546. https://doi.org/10.1177/02783649211056674
Salleh S, Ujang U, Azri S (2022) Topological relationships in R3 for 3D cadastre. Paper presented at the international archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archives
Sereno ME, Robles KE, Kikumoto A, Bies AJ (2020) The effects of three-dimensional context on shape perception. Psychol Sci 31(4):381–396. https://doi.org/10.1177/0956797620901749
Setiawan W (2021) Spatial perception towards social conflicts and the built environment in Indonesia. Int Rev Spat Plan Sustain Dev 9(2):134–150. https://doi.org/10.14246/IRSPSDC.9.2_134
Shojaei D, Kalantari M, Bishop ID, Rajabifard A, Aien A (2013) Visualization requirements for 3D cadastral systems. Comput Environ Urban Syst 41:39–54. https://doi.org/10.1016/j.compenvurbsys.2013.04.003
Shojaei D, Olfat H, Rajabifard A, Briffa M (2018) Design and development of a 3D digital cadastre visualization prototype. ISPRS Int J Geo Inf 7(10):384. https://doi.org/10.3390/ijgi7100384
Song R, Zhang W, Zhao Y, Liu Y (2022) Unsupervised multi-view CNN for salient view selection and 3D interest point detection. Int J Comput vis 130(5):1210–1227. https://doi.org/10.1007/s11263-022-01592-x
Stiles NRB, Weiland JD, Patel VR (2022) Visual–tactile shape perception in the visually restored with artificial vision. J vis. https://doi.org/10.1167/jov.22.2.14
Thompson LW, Kim B, Zhu Z, Rokers B, Rosenberg A (2021) Perspective cues make eye-specific contributions to 3-D motion perception. J Cogn Neurosci 34(1):192–208. https://doi.org/10.1162/jocn_a_01781
Vázquez PP, Feixas M, Sbert M, Heidrich W (2003) Automatic view selection using viewpoint entropy and its application to image-based modelling. Comput Graph Forum 22(4):689–700. https://doi.org/10.1111/j.1467-8659.2003.00717.x
Vincent J (2022) Perceptual brightness scales in a White’s effect stimulus are not captured by multiscale spatial filtering models of brightness perception. J vis 22(3):20–20. https://doi.org/10.1167/jov.22.3.20
Wang D, Shu H (2022) Accuracy analysis of three-dimensional modeling of a multi-level UAV without control points. Buildings 12(5):592–592. https://doi.org/10.3390/buildings12050592
Wang C, Yu C-B (2021) Design, development and applicability evaluation of a digital cartographic model for 3d cadastre map** in China. ISPRS Int J Geo Inf 10(3):158. https://doi.org/10.3390/ijgi10030158
Wang C, Pouliot J, Hubert F (2017) How users perceive transparency in the 3D visualization of cadastre: testing its usability in an online questionnaire. GeoInformatica 21(3):599–618. https://doi.org/10.1007/s10707-016-0281-y
Ying S, Li L, Guo R (2011) Building 3D cadastral system based on 2D survey plans with SketchUp. Geo-Spat Inf Sci 14(2):129–136. https://doi.org/10.1007/s11806-011-0483-2
Ying S, Li C, Chen N, Jia Y, Guo R, Li L (2021) Object analysis and 3d spatial modelling for uniform natural resources in china. Land. https://doi.org/10.3390/land10111154
Zlatanova S, Rahman AA, Shi W (2004) Topological models and frameworks for 3D spatial objects. Comput Geosci 30(4):419–428. https://doi.org/10.1016/j.cageo.2003.06.004
Acknowledgements
The authors would like to thank the anonymous referees and editor for their valuable comments, which significantly improved this article.
Funding
This research was supported by China National Funds supported this research for NSFC, grant number 42201504, 42230406, Fundamental science (Natural Science) research Project of higher education institutions in Jiangsu Province (22KJB420004).
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Wang, L., Zhou, X., Shen, J. et al. Geovisualization: an optimization algorithm of viewpoint generation for 3D cadastral property units. J Geogr Syst 26, 91–116 (2024). https://doi.org/10.1007/s10109-023-00429-6
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DOI: https://doi.org/10.1007/s10109-023-00429-6