Abstract: Existing point cloud completion networks mainly rely on the spatial coordinates and local neighborhood features of the single-modal point cloud for shape reasoning. However, for some point clouds, there are usually problems such as sparse point distribution and insufficient global contour information. These results in the appearance of missing details and discontinuous topological structures in the completed structure. Previous cross-modal point cloud completion methods strictly rely on paired input multimodal data. The collection and alignment of multimodal data also increase the cost of dataset construction. This limits its direct application in real engineering projects. To address these issues, this paper proposes a network named Dual-State Guided Cross-Modal Point Completion Network (DSG-Net). It aims to make up for the inherent limitations of single-modal features. DSG-Net only uses the partial point cloud input as the original data. During the data preprocessing stage, it generates the corresponding depth map through the preset pinhole camera model for projection and rendering to assist in the completion of the single-modal point cloud. Thus, it overcomes the dependence of traditional cross-modal methods on the strict pairing of multimodal data. The designed Cross-Modal Fusion Generation (CMFG) module utilizes convolution to map the extracted point cloud features and depth map features to the same feature space for concatenation and fusion. Then, the multi-head self-attention mechanism models long-range dependencies in the fused features. And the modeled complementary features are split point by point to generate the initial coarse point cloud. Therefinement stage introduces the Dual-State Guided Refinement (DSGR) module. It comprises two parallel processing channels: structural analysis and similarity comparison. The structural analysis channel measures the spatial correlation between the generated point cloud and the input partial point cloud. This guides the network to focus on structural differences in the missing regions and enhances the continuity of the overall contour and local structures. The similarity comparison channel focuses on mining potential similarity patterns among different local regions. It guides topological refinement in the missing regions, thereby improving the shape consistency and detail completeness of the completed point cloud. The State Flow Block (SFBlock) is integrated within the DSGR module. It enables dynamic feature-state updating and effective information propagation during the iterative refinement process. This module adaptively controls the retention ratios of historical accumulated states and current input features via a dual-gating unit. This mechanism dynamically adjusts local details and positional deviations during the refinement process. It also prevents the recovered shape information from being weakened or redundantly propagated across multiple iterations. To verify the effectiveness of DSG-Net, experiments are conducted on two public datasets, PCN and ShapeNet55, and a self-built Terracotta Warrior dataset. The experimental results show that DSG-Net achieves an average CD-L1 of 6.72 on the public PCN dataset. This corresponds to a relative reduction of 19.81% compared with PoinTr’s average CD-L1 of 8.38. Meanwhile, DSG-Net outperforms the latest network, 3DMambaComplete, in terms of CD-L1 across all eight categories. These results further demonstrate that DSG-Net can effectively improve the structural completion accuracy of missing regions. On the ShapeNet55 dataset, DSG-Net achieves an average CD-L2 of 0.89 across three tasks with different difficulty levels. Meanwhile, among the five common categories, DSG-Net obtains the best completion performance in three of them. On the self-built Terracotta Warriors dataset, DSG-Net achieves an average CD-L1 of 5.23. This corresponds to a relative reduction of 10.45% compared with PoinTr’s average CD-L1 of 5.84. This indicates that DSG-Net has strong generalization ability on non-public data. In summary, DSG-Net achieves favorable completion performance on both public and self-built datasets. This indicates that it can not only effectively restore the shape structure of complex objects, but also exhibit strong cross-dataset adaptability. Therefore, it can provide valuable technical reference for future cultural heritage digital modeling and incomplete artifact restoration.

摘要： 现有点云补全网络主要依赖单模态点云自身的空间坐标和局部邻域特征进行形状推理，但部分点云通常存在点分布稀疏以及全局轮廓信息不足的问题，难以准确判断缺失区域的空间方向和结构边界，导致补全结构出现细节缺失和拓扑结构不连续的问题。以往跨模态点云补全方法需严格依赖于成对输入的多模态数据，多模态数据的采集和配准过程也会增加数据构建成本，难以直接在现实工程中应用。为此，提出一种基于双状态引导的跨模态点云补全网络（DSG-Net），旨在弥补单模态特征的固有表征不足。DSG-Net仅以输入进来的部分点云作为原始数据，在数据预处理阶段通过预设的针孔相机模型投影渲染生成对应的深度图来辅助单模态点云进行补全，从而摆脱了传统跨模态方法对严格配对多模态数据的依赖。首先，设计的跨模态融合生成模块（CMFG）将提取出来的点云特征和深度图特征利用卷积映射至相同的特征空间进行拼接融合，再通过多头自注意力机制将融合特征进行长距离建模，并将建模后的互补特征通过逐点拆分初步生成粗点云。其次，细化阶段引入双状态引导细化模块（DSGR），其包含结构分析与相似性比对两条并行处理通道。结构分析通道通过度量生成点云与输入部分点云之间的空间相关性，引导网络重点关注缺失区域的结构差异，以提升整体轮廓和局部结构的连续性。相似性比对通道则侧重于挖掘不同局部区域之间的潜在相似模式，引导缺失区域的拓扑优化，从而进一步增强补全点云的形态一致性与细节完整性。最后，DSGR模块内部集成状态流动模块（SFBlock），用于实现在迭代细化过程中特征状态的动态更新与有效传递。SFBlock模块通过双门控单元分别控制历史积累状态和当前输入特征的保留比例，可以动态调控细化过程中的局部细节和位置偏差，避免已恢复的形状信息在多次迭代中被削弱或冗余。为验证DSG-Net网络的有效性，在两个公开数据集PCN、ShapeNet55以及一个自建的兵马俑数据集上进行实验评估。实验结果表明，DSG-Net在公开PCN数据集上的平均CD-L1为 6.72，比PoinTr（8.38）的平均CD-L1相对降低19.81%。同时，DSG-Net在8个类别上的CD-L1指标均优于最新网络 3DMambaComplete，进一步说明了DSG-Net网络能够有效提升缺失区域的结构补全精度。在ShapeNet55数据集上，DSG-Net 在三种不同难度任务中的平均CD-L2达到 0.89，同时，在常见的五个类别中，DSG-Net在其中三类上取得最优补全效果。在自建兵马俑数据集上，DSG-Net的平均CD-L1为5.23，比PoinTr（5.84）的平均CD-L1相对降低10.45%，表明DSG-Net网络在非公开数据上具有较强的泛化性。综上，DSG-Net在公开数据集与自建数据集上均取得了良好的补全效果，说明其不仅能够有效恢复复杂物体的形状结构，还具有较强的跨数据集适应能力，可为后续文化遗产数字化建模与残缺文物修复提供有价值的技术参考。