  Neural implicit surface reconstruction has become a new trend in
reconstructing a detailed 3D shape from images. In previous methods, however,
the 3D scene is only encoded by the MLPs which do not have an explicit 3D
structure. To better represent 3D shapes, we introduce a volume encoding to
explicitly encode the spatial information. We further design hierarchical
volumes to encode the scene structures in multiple scales. The high-resolution
volumes capture the high-frequency geometry details since spatially varying
features could be learned from different 3D points, while the low-resolution
volumes enforce the spatial consistency to keep the shape smooth since adjacent
locations possess the same low-resolution feature. In addition, we adopt a
sparse structure to reduce the memory consumption at high-resolution volumes,
and two regularization terms to enhance results smoothness. This hierarchical
volume encoding could be appended to any implicit surface reconstruction method
as a plug-and-play module, and can generate a smooth and clean reconstruction
with more details. Superior performance is demonstrated in DTU, EPFL, and
BlendedMVS datasets with significant improvement on the standard metrics.
