标题： 稀疏视图三维重建：最新进展与开放挑战 显示英文标题

标题： Sparse-View 3D Reconstruction: Recent Advances and Open Challenges

摘要： 稀疏视图三维重建在密集图像采集不切实际的应用中至关重要，例如机器人技术、增强现实/虚拟现实（AR/VR）和自主系统。在这些情况下，最小的图像重叠阻止了可靠的对应匹配，导致传统方法如基于运动的结构（SfM）和多视图立体（MVS）失败。本综述介绍了神经隐式模型（例如NeRF及其正则化版本）、基于显式点云的方法（例如3D高斯点绘制）以及利用扩散和视觉基础模型（VFMs）先验的混合框架的最新进展。我们分析了几何正则化、显式形状建模和生成推理如何用于减轻稀疏视图设置中的伪影，如浮动物和姿态模糊。在标准基准上的比较结果揭示了重建精度、效率和泛化之间的关键权衡。与以往的综述不同，我们的综述提供了一个统一的观点，涵盖基于几何、神经隐式和生成（基于扩散）方法。我们强调了领域泛化和无姿态重建的持续挑战，并概述了开发3D原生生成先验和实现实时、无约束的稀疏视图重建的未来方向。 显示英文摘要

摘要： Sparse-view 3D reconstruction is essential for applications in which dense image acquisition is impractical, such as robotics, augmented/virtual reality (AR/VR), and autonomous systems. In these settings, minimal image overlap prevents reliable correspondence matching, causing traditional methods, such as structure-from-motion (SfM) and multiview stereo (MVS), to fail. This survey reviews the latest advances in neural implicit models (e.g., NeRF and its regularized versions), explicit point-cloud-based approaches (e.g., 3D Gaussian Splatting), and hybrid frameworks that leverage priors from diffusion and vision foundation models (VFMs).We analyze how geometric regularization, explicit shape modeling, and generative inference are used to mitigate artifacts such as floaters and pose ambiguities in sparse-view settings. Comparative results on standard benchmarks reveal key trade-offs between the reconstruction accuracy, efficiency, and generalization. Unlike previous reviews, our survey provides a unified perspective on geometry-based, neural implicit, and generative (diffusion-based) methods. We highlight the persistent challenges in domain generalization and pose-free reconstruction and outline future directions for developing 3D-native generative priors and achieving real-time, unconstrained sparse-view reconstruction.