PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction

TL;DR

PlantStereo introduces a large, high-accuracy stereo dataset for plant surface reconstruction, improving deep learning model training and addressing limitations of existing datasets.

Contribution

The paper presents a new large-scale plant stereo dataset with high-accuracy disparity ground truth, facilitating better model training for plant surface dense reconstruction.

Findings

High accuracy disparity images improve deep learning training effectiveness.

Extensive experiments demonstrate dataset's utility across different models and plants.

PlantStereo enables more reliable plant surface dense reconstruction.

Abstract

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