This study presents a clustering model of indoor point cloud density based on an exponential function. First, the cutoff distance function model is developed according to the distance and angular resolution of point clouds. Second, the local density model based on the exponential function is constructed by analyzing the number of points and distance mean and standard deviation. Third, according to the distance between the point cloud and boundary, the constraint distance density of judging a wall is obtained. Similarly, the density function of the z value is constructed according to the amplitude distribution and the exponential function of the z value. Combined with the local density, the density clustering model of walls, ceilings, and floors is obtained. For indoor objects, the constraint distance is determined according to the local density within the cutoff distance. In addition, the clustering center can be determined according to the product of the constraint distance and local density. Finally, indoor targets are clustered according to the clustering attribute of each point. Based on the density clustering model, walls, ceilings, floors, and objects in the room can be extracted. The proposed method is compared with other clustering algorithms in different indoor scenarios, and the results show that the number of objects extracted using the proposed method is greater than that extracted using the CFDP and DPC methods. In addition, when there are a few noise points between adjacent targets, the extraction effect of the proposed method is better than that of the CFDP and DPC methods. Furthermore, accuracy, recall, and F1-score are used to evaluate the object extraction performance of the proposed method, which varies with types of rooms. The results show that the proposed method is more suitable for rooms with non-adjacent objects, and its performance is related to the closeness of adjacent objects. Given the shortcomings of the proposed method, future research work will focus on the extraction of objects close to each other. In addition, a future clustering algorithm can accurately extract some small items on other objects, such as books or cups on a table.