# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 1 Introduction
However, this method only considers the global features of the point cloud and uses fixed convolution, which cannot adapt to multi-scale geometric structures. As a result, it neglects the local detail features of incomplete point clouds, leading to poor performance in local detail completion. To address the issue of dynamic feature fusion in existing Transformer-based point cloud completion models such as PoinTr [13] and AdaPoinTr [14], it is important to note that these models rely on fixed domain partitioning, which prevents them from adaptively adjusting local aggregation weights. This limitation leads to significant deviations in results for certain categories. For instance, on the KITTI dataset, the lack of consideration for occlusion patterns results in large FD errors. Additionally, methods such as autoencoders [15] and 3D point capsule networks [16] tend to focus heavily on global features, neglecting the local geometric information of the incomplete point cloud. As a result, the generated point cloud often captures only common features of a particular object type, failing to account for the distinctive features of individual objects. [PERSON] et al. [17] proposed the PF-Net, a point fractal network designed to effectively capture local features through a multi-scale feature extraction module. This network predicts the missing parts of the point cloud from coarse to fine resolution. However, its reliance on dense point cloud up-sampling to gather more local information significantly increases computational demands. Building on the work, this paper introduces a multi-scale point cloud completion network termed local neighborhood dynamic fusion (LNDM-Net). The network utilizes a local neighborhood dynamic approach combined with an attention mechanism to extract three different scales of the original point cloud, effectively capturing both local and global features of incomplete point clouds. This dual extraction minimizes the loss of local information during the feature extraction process. Furthermore, a pyramid point cloud generator is employed to facilitate multi-stage point cloud completion, progressing from coarse to fine levels. To further enhance completion accuracy, an attention discriminator is incorporated into the model. The specific contributions of this paper are outlined as follows:  
(1) Design of a Multi-Scale Point Cloud Extractor: A novel multi-scale point cloud extractor based on local neighborhood dynamic fusion is proposed. Through farthest point sampling (FPS) for point cloud down-sampling, we generate point clouds at three distinct scales. These multi-scale point clouds are then processed using a Transformer architecture to further enhance feature extraction capabilities. The local neighborhoods around key points in the incomplete point cloud are dynamically fused, and a biased attention mechanism is employed to retain the local feature information at each scale. This improves the network's ability to extract both global and local features from the original point cloud. (2) Development of a Multi-Scale Attention-Based Point Cloud Generator: A point cloud generator that leverages a multi-scale attention layer and pyramid structure is introduced. This approach progressively refines local details from coarse to fine resolutions, effectively enhancing point cloud completion. When combined with a generative adversarial network (GAN), a single-scale attention discriminator is incorporated, which further boosts the completion quality of the generated point cloud. (3) Experimental Validation: The experimental results demonstrate that LNDM-Net achieves promising completion performance on the PCN and ShapeNet public datasets. Ablation studies confirm the effectiveness of the innovative techniques proposed in this paper, while generalization tests show that LNDM-Net exhibits robustness when applied to a variety of sample types.