The indoor scene generation task is an important research topic in recent years. It can not only provide a natural annotated indoor scene dataset for computer vision tasks to help better understand the scene, but also can be applied to many real scenes such as robot navigation. The diversity of indoor scene layouts makes scene generation a very challenging task. This paper reviews the recent research progress in the field of indoor scene generation, summarizes and classifies the generation algorithms in terms of scene input, scene generation method, scene representation, scene generation order, and scene context relationship. The three categories of the generation algorithms including sample-free generation method based on object relationship, sample-free generation method based on human activities, and sample-based object relationship based on object relationship are analyzed with advantages and disadvantages. In addition, this article also summarizes the limitations of the existing algorithms and points out the direction that can be explored in the field of indoor scene generation in the future.