Kumquat is a kind of indoor ornamental plant that is deeply loved by consumers. The number and spatial distribution of its fruits are important indicators that determine the quality and sales price of kumquat. The recognition methods based on RGB images or single-view point clouds were difficult to accurately complete the calculation of the total fruits amount of the whole plant, and could not comprehensively display the three-dimensional spatial distribution of fruits. Therefore, a fruit recognition method based on point cloud registration was proposed to solve the problems of fruit recognition and total counting of the whole plant. Firstly, plants were placed on a rotating platform, and a low-cost RGB-D camera was used to collect the point cloud of plants at six angles for 60° every interval. The background was removed according to the spatial distance. The outlier noise was removed by radius filtering algorithm. The white color noise was removed based on the color information. And the “flying pixels” and edge noises were removed according to normal vector features and Euclidean clustering algorithm. Based on the random sampling consensus algorithm, the cylindrical point cloud of the rotating platform was segmented and the central axis was calculated. The point cloud was rotated around the central axis by a corresponding angle for initial registration. Then the point-to-plane ICP algorithm was used for accurate registration. Finally, Euclidean clustering algorithm was used to divide the plant point cloud into multiple clusters. And the spherical segmentation of each cluster was performed based on the random sampling consensus algorithm. The segmented spherical point clouds were the identified fruits, and its three-dimensional spatial distribution could be displayed according to the center and radius of the sphere. Totally nine potted kumquat plants (149 fruits in total) were identified in the fruit growing stage. The results showed that the total recall was 85.91%, precision was 79.01% and F1 value was 82.32%. Compared with the ground truth, the coefficient of determination and mean absolute percentage error of the number of fruits calculated by the proposed method were 0.97 and 16.02%, respectively. The experimental results showed that the proposed method was independent of color information and could effectively recognition immature green fruits in the whole plant, which could provide a reference for fruit identification and yield estimation.