For the natural scenes, the point clouds of plants or organs acquired by sensors are incomplete due to the problems of occlusion, viewpoint limitation and improper operation. A multi-scale feature extraction model with point cloud pyramid decoder (MSF-PPD) network was proposed for leaf shape complementation. Firstly, the multi-scale feature extraction module was used to achieve the global extraction and fusion of different dimensional feature information, and secondly, the multi-stage generation of leaf point cloud was complemented by the point cloud pyramid decoder to finally obtain the complete target leaf shape. A library of Epipremnum aureum leaf simulation models was constructed by using surface parametric equations and discretized into point clouds as the training set and validation set for network model training, and the Epipremnum aureum leaf point clouds were obtained by using the Kinect v2 camera as the test set for model complementary performance evaluation. The experimental results showed that the network structure had an ideal effect on leaf point cloud complementation, which proved that the method proposed was able to perform efficient and complete complementation of Epipremnum aureum leaf under the obscured situation.