Global solvers have emerged as a powerful paradigm for 3D vision, offering certifiable solutions to nonconvex geometric optimization problems traditionally addressed by local or heuristic methods. This survey presents the first systematic review of global solvers in geometric vision, unifying the field through a comprehensive taxonomy of three core paradigms: Branch-and-Bound (BnB), Convex Relaxation (CR), and Graduated Non-Convexity (GNC). We present their theoretical foundations, algorithmic designs, and practical enhancements for robustness and scalability, examining how each addresses the fundamental nonconvexity of geometric estimation problems. Our analysis spans ten core vision tasks, from Wahba problem to bundle adjustment, revealing the optimality-robustness-scalability trade-offs that govern solver selection. We identify critical future directions: scaling algorithms while maintaining guarantees, integrating data-driven priors with certifiable optimization, establishing standardized benchmarks, and addressing societal implications for safety-critical deployment. By consolidating theoretical foundations, practical advances, and broader impacts, this survey provides a unified perspective and roadmap toward certifiable, trustworthy perception for real-world applications. A continuously-updated literature summary and companion code tutorials are available at https://github.com/ericzzj1989/Awesome-Global-Solvers-for-3D-Vision.

翻译：全局求解器已成为三维视觉领域的一种强大范式，为传统上采用局部或启发式方法解决的非凸几何优化问题提供了可验证的解决方案。本综述首次对几何视觉中的全局求解器进行了系统性回顾，通过将三大核心范式——分支定界（BnB）、凸松弛（CR）和渐进非凸性（GNC）——纳入一个全面的分类体系，统一了该领域。我们阐述了它们的理论基础、算法设计以及为提升鲁棒性和可扩展性所做的实际改进，并分析了每种方法如何应对几何估计问题固有的非凸性。我们的分析涵盖了从Wahba问题到光束法平差等十项核心视觉任务，揭示了影响求解器选择的最优性-鲁棒性-可扩展性权衡关系。我们指出了关键的未来研究方向：在保持可验证性的前提下扩展算法规模、将数据驱动的先验知识与可验证优化相结合、建立标准化基准测试，以及应对安全关键型部署的社会影响。通过整合理论基础、实际进展和更广泛的影响，本综述为面向实际应用的可验证、可信赖的感知技术提供了统一的视角和发展路线图。持续更新的文献总结及配套代码教程可在 https://github.com/ericzzj1989/Awesome-Global-Solvers-for-3D-Vision 获取。