# PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction

arXiv: https://arxiv.org/abs/2111.15192 · HTML: https://mlsanity.com/paper/2111.15192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Group-wise Correlation Stereo Network](https://mlsanity.com/paper/1903.04025/) (2019)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2011.02910/) (2020)
- [Hierarchical Deep Stereo Matching on High-resolution Images](https://mlsanity.com/paper/1912.06704/) (2019)
- [Computing the Stereo Matching Cost with a Convolutional Neural Network](https://mlsanity.com/paper/1409.4326/) (2014)
- [Normal Assisted Stereo Depth Estimation](https://mlsanity.com/paper/1911.10444/) (2019)
- [Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset](https://mlsanity.com/paper/2003.11172/) (2020)
- [EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching](https://mlsanity.com/paper/1803.05196/) (2018)
- [Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling](https://mlsanity.com/paper/1612.04770/) (2016)
- [Semi-synthesis: A fast way to produce effective datasets for stereo matching](https://mlsanity.com/paper/2101.10811/) (2021)

## Abstract (arXiv)

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