# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 8 Conclusions  
This paper introduces a novel approach that leverages topological perception theory to enhance the retrieval of intricate landmark building models. Grounded in the topological perception theory, the paper categorizes model views into four classes and further stratifies typical 3D landmark building models into six categories by amalgamating the top-view structure with the side-view structure. Simultaneously, a new topology calculation method is proposed to address the insensitivity of the quotient distance histogram to certain topological structures. To ensure reliable outcomes, the interval parameter of the quotient distance histogram is readjusted to mitigate confusion between non-concave shapes and concave unclosed shapes with partially concave features. Additionally, an angular distribution feature is introduced to resolve confusion between holes and concave unclosed views.  
In addition, this paper also proposes a new projection method that is based on the stability of topological features, the fact that most of the models have the correct upright orientation, and the reality that most of the significant topological features appear on the top surface and sides of the model; determines that too many views are not meaningful for the extraction of topological features of the model; and reduces the views of the landmark building model. Thus, the model projection time and the subsequent extraction of view features can be reduced. Finally, we also put the spatial information to use by storing the features in an orderly manner through the determined vertical direction, which facilitates the feature fusion of the top and side views.  
Experimental results from image retrieval showcase the enhanced performance of the QDAH algorithm in topology retrieval. Two refinements to the QDH algorithm prove effective in reliably classifying views based on topological structure. Model retrieval experiments affirm the feasibility of the proposed projection method combined with the QDAH algorithm for model retrieval. In the classification of landmark building models according to topological structure, the algorithm in this paper surpasses the SPH, LFD, MVCNN, and GVCNN algorithms and demonstrates similarity to the LFD_QHDA algorithm. This further substantiates the superior capability of the QDAH algorithm in extracting topological features from models. The findings also underscore that a moderate reduction in views is viable when extracting topological features of 3D models. Although more views offer additional information, they also introduce redundant features, particularly when applied to relatively stable features such as topology, rendering excessive views less meaningful.  
Conceptualization, Y.D.; methodology, C.L. and Y.D.; validation, C.L.; formal analysis, C.L., Y.D. and Y.Y.; writing--original draft preparation, C.L. and Y.D.; writing--review and editing, C.L., Y.D. and Y.Y.; supervision, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under MOST grant No. 42071346, No. 42371397, and Marine Science and Technology Innovation Project of Jiangsu Province grant No. JSZRHYKJ202302.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.