In the training process of the implicit 3D reconstruction network, the choice of spatial query points' sampling strategy affects the final performance of the model. Different works have differences in the selection of sampling strategies, not only in the spatial distribution of query points but also in the order of magnitude difference in the density of query points. For how to select the sampling strategy of query points, current works are more akin to an enumerating operation to find the optimal solution, which seriously affects work efficiency. In this work, we explored the relationship between sampling strategy and network final performance through classification analysis and experimental comparison from three aspects: the relationship between network type and sampling strategy, the relationship between implicit function and sampling strategy, and the impact of sampling density on model performance. In addition, we also proposed two methods, linear sampling and distance mask, to improve the sampling strategy of query points, making it more general and robust.

翻译：在隐式三维重建网络的训练过程中，空间查询点的采样策略选择会影响模型的最终性能。不同工作在采样策略的选择上存在差异，不仅体现在查询点的空间分布上，还体现在查询点密度的数量级差异上。当前工作中，如何选择查询点的采样策略更像是一种枚举式寻找最优解的操作，严重影响工作效率。本研究从三个方面，即网络类型与采样策略的关系、隐式函数与采样策略的关系以及采样密度对模型性能的影响，通过分类分析和实验对比，探索了采样策略与网络最终性能之间的关系。此外，我们还提出了两种方法，即线性采样和距离掩码，以改进查询点的采样策略，使其更具通用性和鲁棒性。