Domain adaptive point cloud completion (DA PCC) aims to narrow the geometric and semantic discrepancies between the labeled source and unlabeled target domains. Existing methods either suffer from limited receptive fields or quadratic complexity due to using CNNs or vision Transformers. In this paper, we present the first work that studies the adaptability of State Space Models (SSMs) in DA PCC and find that directly applying SSMs to DA PCC will encounter several challenges: directly serializing 3D point clouds into 1D sequences often disrupts the spatial topology and local geometric features of the target domain. Besides, the overlook of designs in the learning domain-agnostic representations hinders the adaptation performance. To address these issues, we propose a novel framework, DAPointMamba for DA PCC, that exhibits strong adaptability across domains and has the advantages of global receptive fields and efficient linear complexity. It has three novel modules. In particular, Cross-Domain Patch-Level Scanning introduces patch-level geometric correspondences, enabling effective local alignment. Cross-Domain Spatial SSM Alignment further strengthens spatial consistency by modulating patch features based on cross-domain similarity, effectively mitigating fine-grained structural discrepancies. Cross-Domain Channel SSM Alignment actively addresses global semantic gaps by interleaving and aligning feature channels. Extensive experiments on both synthetic and real-world benchmarks demonstrate that our DAPointMamba outperforms state-of-the-art methods with less computational complexity and inference latency.

翻译：领域自适应点云补全（DA PCC）旨在缩小带标签源域与无标签目标域之间的几何和语义差异。现有方法因使用CNN或视觉Transformer而存在感受野有限或二次复杂度的问题。本文首次研究了状态空间模型（SSMs）在DA PCC中的适应性，发现直接将SSMs应用于DA PCC会面临若干挑战：将3D点云直接序列化为1D序列往往会破坏目标域的空间拓扑和局部几何特征。此外，在学习领域无关表示的设计上存在疏忽，阻碍了自适应性能。为解决这些问题，我们提出了一种新颖的框架——DAPointMamba，用于DA PCC，该框架展现出强大的跨领域适应性，并具备全局感受野和高效线性复杂度的优势。它包含三个创新模块：具体而言，跨领域块级扫描通过引入块级几何对应关系，实现有效的局部对齐；跨领域空间SSM对齐通过基于跨领域相似性调制块特征，进一步增强空间一致性，有效缓解细粒度结构差异；跨领域通道SSM对齐通过交错和对齐特征通道，主动处理全局语义鸿沟。在合成和真实世界基准测试上的大量实验表明，我们的DAPointMamba在计算复杂度和推理延迟更低的情况下，性能优于现有最先进方法。