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 masking, to improve the sampling strategy of query points, making it more robust.

本文研究了隐式三维重建网络中空间查询点采样策略对模型性能的影响，在网络类型、隐式函数和采样密度三个方面进行分类分析和实验比较，提出了线性采样和距离掩模两种改进策略。

暗含式三维重建采样策略分析