Reconstructing meshes from point clouds is a fundamental task in computer vision with applications spanning robotics, autonomous systems, and medical imaging. Selecting an appropriate learning-based method requires understanding trade-offs between computational efficiency, geometric accuracy, and output constraints. This paper categorizes over fifteen methods into five paradigms -- PointNet family, autoencoder architectures, deformation-based methods, point-move techniques, and primitive-based approaches -- and provides practical guidance for method selection. We contribute: (1) a decision framework mapping input/output requirements to suitable paradigms, (2) a failure mode analysis to assist practitioners in debugging implementations, (3) standardized comparisons on ShapeNet benchmarks, and (4) a curated list of maintained codebases with implementation resources. By synthesizing both theoretical foundations and practical considerations, this work serves as an entry point for practitioners and researchers new to learning-based 3D mesh reconstruction.

翻译：从点云重建网格是计算机视觉领域的一项基础任务，其应用涵盖机器人学、自主系统和医学成像。选择合适的学习方法需要理解计算效率、几何精度和输出约束之间的权衡。本文将超过十五种方法归纳为五大范式——PointNet系列、自编码器架构、基于变形的方法、点移动技术和基于基元的方法——并为方法选择提供实用指导。我们的贡献包括：(1) 一个将输入/输出需求映射到合适范式的决策框架，(2) 协助实践者调试实现的故障模式分析，(3) 在ShapeNet基准测试上的标准化比较，以及(4) 一份精选的维护代码库及实施资源列表。通过综合理论基础与实践考量，本工作为刚接触基于学习的三维网格重建的实践者和研究者提供了一个入门指南。