LaS-Comp：基于隐空间-空间一致性的零样本三维补全
LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency
把 3D 基础模型的生成先验当作免训练的补全器，用「显式替换 + 隐式精修」两阶段保证观测区域保真与边界无缝，可直接迁移到点云/隐式场补全、稀疏观测重建等任务。
原文依据 an explicit replacement stage that preserves the partial observation geometry to ensure faithful completion
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 LaS-Comp，一种零样本且与类别无关的方法，利用三维基础模型丰富的几何先验，实现对多种类型部分观测的三维形状补全。我们的贡献有三点：第一，LaS-Comp 通过互补的两阶段设计来利用这些强大的生成先验完成补全：(i) 显式替换阶段，保留部分观测的几何结构以确保补全的保真度；(ii) 隐式精修阶段，确保观测区域与合成区域之间的边界无缝衔接。第二，我们的框架无需训练，并且与不同的三维基础模型兼容。第三，我们提出 Omni-Comp，一个综合性基准，结合真实数据与合成数据，并包含多样且具有挑战性的部分观测模式，从而支持更全面、更贴近现实的评测。定量与定性实验均表明，我们的方法优于此前的最优方法。代码与数据将在论文录用后公开。
英文原文摘要 1049 字符
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, LaS-Comp 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. Code and data will be released upon acceptance.