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Stereo matching is an important task in computer vision which has drawntremendous research attention for decades. While in terms of disparityaccuracy, density and data size, public stereo datasets are difficult to meetthe requirements of models. In this paper, we aim to address the issue betweendatasets and models and propose a large scale stereo dataset with high accuracydisparity ground truth named PlantStereo. We used a semi-automatic way toconstruct the dataset: after camera calibration and image registration, highaccuracy 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 onfour different stereo matching methods. Extensive experiments on differentmodels and plants show that compared with ground truth in integer accuracy,high accuracy disparity images provided by PlantStereo can remarkably improvethe training effect of deep learning models. This paper provided a feasible andreliable method to realize plant surface dense reconstruction. The PlantStereodataset and relative code are available at:https://www.github.com/wangqingyu985/PlantStereo

PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction