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In order to obtain the leaf shape parameters of plant leaves efficiently, accurately and automatically, a multi-resolution coded point cloud deep learning network (MRE-PointNet) and autoencoder model based on the Scirpus sibiricum leaf shape parameter estimation algorithm was proposed. The Kinect V2 camera was used to acquire the point cloud data of Scirpus sibiricum leaves in vertical attitude, and the data was pre-processed by straight-pass filtering, segmentation and point cloud simplification algorithm. The geometric model constructed with different parameter combinations was discretized into point cloud data and input into MRE-PointNet network to obtain the pre-training model of the geometric model shape parameter estimation. In order to solve the problem of partial occlusion and noise of the leaves in the filming process, an autoencoder network with secondary processing of the point cloud data was used to obtain the autoencoder pre-training model by taking the discrete point cloud data of the geometric model as input and encoding-decoding operation, which improved the robustness of the MRE-PointNet network in estimating the shape parameters of the occluded data. A total of 300 point clouds of Scirpus sibiricum leaves were collected. With the ratio of 2∶1, totally 200 slices of point cloud data were used as the training set to fine-tune for model transfer to the pre-training model MRE-PointNet, and the remaining 100 slices of point cloud data were used as the test set. By the algorithm, the mathematical statistics and linear regression analysis were performed to compare the estimated and real values of the shape parameters. The experiment results showed that the estimated R2 and RMSE of leaf length were 0.9005 and 0.4170cm, leaf width was 0.9131 and 0.3164cm, and leaf area was 0.9447 and 3.8834cm2, respectively, based on the MRE-PointNet and the self-training model. The encoder model algorithm for estimating the shape parameters of scirpus sibiricum leaves had high precision and practicality.