# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 1 Introduction  
With the rapid advancement of technologies like autonomous driving, 3D reconstruction, and virtual reality, the utilization of 3D point cloud data has become more prevalent [1]. Nonetheless, the acquisition of point cloud data can be hindered by challenges such as object occlusion and reflective surfaces, leading to sparse and incomplete datasets. This lack of completeness poses difficulties in accurately capturing both geometric and semantic details, which in turn severely impacts downstream tasks like point cloud classification, semantic segmentation, and object detection [2]. In the context of autonomous driving, precise 3D scene comprehension is vital, and any loss of data could potentially introducesafety risks. Consequently, researching effective methods for completing point cloud data collected from real-world 3D environments holds significant importance [3]. Traditional point cloud completion techniques predominantly focus on geometric properties and model-driven completion strategies. These methods typically involve extracting geometric features from the incomplete point cloud's boundaries and employing techniques like surface reconstruction, interpolation, and smoothing to restore missing structures, especially in geometrically sensitive areas. [PERSON] et al. [4] addressed the completion of incomplete point clouds by identifying potential symmetry patterns within the input data and applying these patterns to guide the reconstruction. Similarly, [PERSON] et al. [5] leveraged symmetry priors for completion, utilizing the inherent symmetry in the data model. Another approach, template matching, involves comparing partial inputs with a database of template models to complete the shape. [PERSON] et al. [6] introduced a database model matching-based approach for point cloud completion. While this method benefits from using a large array of sample data and diverse databases for shape restoration, it suffers from challenges such as the complexity of building databases and limited sample diversity. These traditional methods are constrained by their heavy reliance on prior knowledge and limited accuracy, making them less applicable for point cloud completion in real-world 3D settings. In recent years, following the success of PointNet [7] and PointNet++ [8] in enabling segmentation and classification tasks through deep learning models directly applied to point cloud data, the use of deep learning for point cloud processing has gained significant attention. In point cloud completion tasks, deep learning models are employed to learn the features and distributions of large-scale point cloud datasets to facilitate data completion. [PERSON] et al. [7,8] introduced a pioneering deep learning model capable of directly handling point cloud data, allowing unstructured point cloud data to be processed within deep learning frameworks. This advancement addressed the challenges posed by the irregular and unordered nature of input point cloud data, which had previously hindered effective processing. [PERSON] et al. [9] proposed the innovative LGAN-AE network, which incorporates a generative adversarial network (GAN) module into an autoencoder. While this approach improves point cloud completion, it still struggles with generating detailed local features of the point cloud. [PERSON] et al. [10] introduced the PCT network, which refines the attention mechanism and adapts the Transformer architecture for more effective point cloud data processing. [PERSON] et al. [11] employed a lightweight DGCNN for feature extraction, integrating a Transformer encoder for both feature extraction and completion. However, the use of a fixed convolution kernel to extract local edge features limits the model's ability to dynamically adjust to the complex geometric structures inherent in incomplete point clouds. [PERSON] et al. [12] proposed the PCN, which predicts and generates point cloud data through encoding-decoding operations.