  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.
