  Stereo matching is an important task in computer vision which has drawn
tremendous research attention for decades. While in terms of disparity
accuracy, density and data size, public stereo datasets are difficult to meet
the requirements of models. In this paper, we aim to address the issue between
datasets and models and propose a large scale stereo dataset with high accuracy
disparity ground truth named PlantStereo. We used a semi-automatic way to
construct the dataset: after camera calibration and image registration, high
accuracy disparity images can be obtained from the depth images. In total,
PlantStereo contains 812 image pairs covering a diverse set of plants: spinach,
tomato, pepper and pumpkin. We firstly evaluated our PlantStereo dataset on
four different stereo matching methods. Extensive experiments on different
models and plants show that compared with ground truth in integer accuracy,
high accuracy disparity images provided by PlantStereo can remarkably improve
the training effect of deep learning models. This paper provided a feasible and
reliable method to realize plant surface dense reconstruction. The PlantStereo
dataset and relative code are available at:
https://www.github.com/wangqingyu985/PlantStereo
