  In this paper, we propose the Masked Space-Time Hash encoding (MSTH), a novel
method for efficiently reconstructing dynamic 3D scenes from multi-view or
monocular videos. Based on the observation that dynamic scenes often contain
substantial static areas that result in redundancy in storage and computations,
MSTH represents a dynamic scene as a weighted combination of a 3D hash encoding
and a 4D hash encoding. The weights for the two components are represented by a
learnable mask which is guided by an uncertainty-based objective to reflect the
spatial and temporal importance of each 3D position. With this design, our
method can reduce the hash collision rate by avoiding redundant queries and
modifications on static areas, making it feasible to represent a large number
of space-time voxels by hash tables with small size.Besides, without the
requirements to fit the large numbers of temporally redundant features
independently, our method is easier to optimize and converge rapidly with only
twenty minutes of training for a 300-frame dynamic scene.As a result, MSTH
obtains consistently better results than previous methods with only 20 minutes
of training time and 130 MB of memory storage. Code is available at
https://github.com/masked-spacetime-hashing/msth
