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Aiming at establishing a 3D point cloud model of fruit tree with true color to provide scientific guidance for the production management of orchard, a research on the registration method for two pieces of 3D original point clouds of fruit tree obtained from different perspectives was carried out. The 3D raw point clouds of apple tree in two perspectives were obtained based on Kinect camera and information fusion technology. Firstly, the background removal and noise filtering approaches were used to implement a data pretreatment for each piece of raw point cloud, and every relative exact point cloud of single apple tree was acquired in each specific angle. Secondly, by using depth information of fruit tree’s point cloud image and object boundary characteristics, the key points were extracted based on NARF (Normal aligned radial feature) algorithm. Meanwhile, the FPFH (Fast point feature histograms) descriptor was developed to obtain the characteristic vector for each key point. Thirdly, according to the characteristic vectors, the pairs of corresponding key points between two pieces of point cloud were estimated and extracted. And the spatial mapping relationships between two pieces of point cloud were calculated by validating and refining all pairs of corresponding key points based on the RANSAC (Random sample consensus) algorithm. Then the rotation matrix and translation vector between the two neighboring point clouds were computed, by which, the initial registration of two adjacent pieces of point cloud was achieved further. Finally, on the basis of the initial registration, two pieces of point cloud were fused into the same space coordinate system to complete their precise registration through applying the ICP(Iterative closest point) algorithm. This paper carried out the experiments based on the above algorithms, and the results showed that the improved point cloud registration method could be used to match two pieces of point cloud at any original positions in space, and its mean registration error reached 0.7cm.