This paper introduces LaS-Comp, a zero-shot and category-agnostic approach that leverages the rich geometric priors of 3D foundation models to enable 3D shape completion across diverse types of partial observations. Our contributions are threefold: First, \ourname{} harnesses these powerful generative priors for completion through a complementary two-stage design: (i) an explicit replacement stage that preserves the partial observation geometry to ensure faithful completion; and (ii) an implicit refinement stage ensures seamless boundaries between the observed and synthesized regions. Second, our framework is training-free and compatible with different 3D foundation models. Third, we introduce Omni-Comp, a comprehensive benchmark combining real-world and synthetic data with diverse and challenging partial patterns, enabling a more thorough and realistic evaluation. Both quantitative and qualitative experiments demonstrate that our approach outperforms previous state-of-the-art approaches. Our code and data will be available at \href{https://github.com/DavidYan2001/LaS-Comp}{LaS-Comp}.

翻译：本文提出LaS-Comp，一种零样本且与类别无关的方法，通过利用三维基础模型的丰富几何先验，实现对各类局部观测数据的三维形状补全。我们的贡献包括三方面：首先，LaS-Comp通过互补的两阶段设计利用这些强大的生成先验进行补全：（i）显式替换阶段，保留局部观测几何结构以确保精准补全；（ii）隐式精化阶段，确保观测区域与合成区域之间的无缝边界。其次，我们的框架无需训练且兼容不同三维基础模型。第三，我们提出Omni-Comp综合基准，涵盖真实世界与合成数据，包含多样化且具有挑战性的局部模式，从而支持更全面、更逼真的评估。定量与定性实验均表明，我们的方法优于现有最优方法。代码与数据将发布于\href{https://github.com/DavidYan2001/LaS-Comp}{LaS-Comp}。