  Semantic Scene Completion (SSC) aims to jointly estimate the complete
geometry and semantics of a scene, assuming partial sparse input. In the last
years following the multiplication of large-scale 3D datasets, SSC has gained
significant momentum in the research community because it holds unresolved
challenges. Specifically, SSC lies in the ambiguous completion of large
unobserved areas and the weak supervision signal of the ground truth. This led
to a substantially increasing number of papers on the matter. This survey aims
to identify, compare and analyze the techniques providing a critical analysis
of the SSC literature on both methods and datasets. Throughout the paper, we
provide an in-depth analysis of the existing works covering all choices made by
the authors while highlighting the remaining avenues of research. SSC
performance of the SoA on the most popular datasets is also evaluated and
analyzed.
