# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 2 Related Work  
Three-dimensional model retrieval feature extraction algorithms can be categorized into four main types: statistical feature-based, function transformation-based, view-based, and hybrid feature extraction methods [16; 17; 18; 19]. The view-based feature extraction method generally obtains one or more views by projection, then extracts the features of the views, and finally generates the features of the model through the features of the views. View-based feature extraction methods have gained wide acceptance due to their alignment with human visual perception and their increased resilience to model noise and simplification. [PERSON] [20] introduced a novel approach where an icosahedron is positioned on the model's bounding sphere to generate an 80-hedral polyhedron through a cyclic subdivision algorithm. From this, 42 depth views are derived by projection. Finally, Fourier descriptors are employed for feature extraction. However, this process is computationally expensive. In the experiment conducted by [PERSON] [17], a regular dodecahedron is initially used to enclose the 3D model. Subsequently, the 20 vertices of the dodecahedron serve as viewpoint pairs for acquiring the views. View features are extracted using Zernike moments and Fourier transform descriptors. Retrieval results demonstrate the superiority of this method over many traditional approaches. Nevertheless, there is a large number of redundant views, and it is ignored that the features of different views have different importance to the 3D model. [PERSON] [21] obtained six greyscale images by orthogonally projecting each model onto the six faces of a projection cube containing the model. Subsequently, the Hu invariant moment and Fourier transform descriptor were employed to extract the features of the projected image. In recent years, some researchers have combined deep learning with view-based methods. [PERSON] [22] employed a two-layer convolutional neural network for model classification. The first convolutional neural network layer separately extracts features from 12 2D views, which are then merged and fed into the second neural network layer to obtain a comprehensive shape descriptor. However, this network may not be able to take full advantage of the multi-view feature because it discards non-maximum values in the maximum pooling operation. To address this shortcoming, some scholars have tried to use the long short-term memory (LSTM) network to aggregate the features of multi-view images [23; 24]. In addition, [PERSON] [25] proposed the use of a recurrent attention model to automatically select view sequences for accurate 3D shape recognition. [PERSON] [26] integrated light field descriptors with clustering to generate representative views, followed by feature extraction using a convolutional neural network. However, the majority of existing shape descriptors are designed for generic 3D models. The diverse architectural styles pose a significant challenge to effective shape retrieval for landmark building models. In contrast to conventional 3D building models, 3D landmark building models exhibit more pronounced topological structures, including holes, separations, and other unique shapes, resulting in higher matching complexity. Therefore, this paper introduces a novel shape retrieval method based on topological perception theory for retrieving 3D landmark building models. The primary contributions of this paper encompass three key aspects: Firstly, based on the theory of topological perception, this paper abstracts and simplifies the landmark building model into a simple topological structure and reclassifying landmark building model from a new perspective. Secondly, addressing the limitation of previous shape descriptors in adequately capturing topological perception features, we propose a novel feature extraction method employing the quotient distance-angle histogram (QDAH) to extract topological features, thereby enhancing sensitivity to distinct topological perception structures.