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.

翻译：3D系列大规模数据集增加之后的过去几年中,南南合作在研究界获得了巨大的动力,因为它面临尚未解决的挑战。具体地说,南南合作在于大面积未观测到的区域的模糊完成和地面真相的薄弱监督信号。这导致关于此事的文件数量大幅增加。这项调查旨在查明、比较和分析技术,对南南合作关于方法和数据集的文献进行批判性分析。我们在整个文件中深入分析了现有工作,涵盖作者所作的所有选择,同时强调了其余的研究途径。还评估并分析了空间局在最受欢迎的数据集方面的绩效。