In the rapidly advancing field of automated building assessment, the accurate generation of floorplans from point cloud data is crucial, particularly for residential buildings which form a significant part of the urban environment. This study presents detailed evaluation of the latest state-of-the-art methods in automated floorplan generation, focusing exclusively on their application in indoor residential buildings. Our analysis assesses these methods using a diverse range of metrics, including accuracy, efficiency, and scalability, to understand their performance in interpreting complex residential environments. We evaluate two approaches, uncovering their strengths and shortcomings in various scenarios. The results of this comparative study are critical; they not only highlight the current capabilities and limitations of these methods but also pave the way for future enhancements. Our findings provide valuable insights for both academic researchers and industry professionals, emphasizing the need for further innovation and precision in the field of automated residential floorplan generation. This work contributes to the ongoing development of automated building assessment methodologies, aiming to optimize the process of transforming point cloud data into accurate and functional floorplans for residential buildings.