© 2017 IEEE.Scene detection via processing of multimedia data is a significant research area for the advancement of the video technologies and applications. Currently, the scene detection is mostly performed manually. Thus, it is time consuming and costly. Therefore, it is important to develop algorithms that can automatically segment scenes to support the advancement of these technologies and applications. With the wide-spread utilization of the 3 Dimensional (3D) videos, researchers working in the field of the video scene detection start using them in this field as well. However, there is still a gap in the application of the scene detection algorithms to Depth Maps (DMs) that are a part of the 3D video and important for temporal video scene detection. In this study, dominant clusters and K-means method is proposed to detect the temporal 3D video segments using the DMs. The experimental studies performed using the scene detection method present that the video scenes can be edited efficiently without human assistance. Moreover, unlike similar studies in the literature, the proposed method can provide successful results on video sequences thanks to the dominant clusters and the K-means clustering approach utilized.