# CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation

arXiv: https://arxiv.org/abs/1804.01967 · HTML: https://mlsanity.com/paper/1804.01967/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 8 of 8)

- [MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching](https://mlsanity.com/paper/2108.09770/) (2021)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo](https://mlsanity.com/paper/2205.14320/) (2022)
- [FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks](https://mlsanity.com/paper/2110.02582/) (2021)
- [DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching](https://mlsanity.com/paper/2304.08056/) (2023)
- [TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation](https://mlsanity.com/paper/2111.12502/) (2021)
- [Stereo Matching with Cost Volume based Sparse Disparity Propagation](https://mlsanity.com/paper/2201.11937/) (2022)
- [Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching](https://mlsanity.com/paper/2412.18703/) (2024)

## Abstract (arXiv)

Recently, there has been a paradigm shift in stereo matching with learning-based methods achieving the best results on all popular benchmarks. The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions. Many of these assumptions, however, had been validated extensively and hold for the majority of possible inputs. In this paper, we generate a matching volume leveraging both data with ground truth and conventional wisdom. We accomplish this by coalescing diverse evidence from a bidirectional matching process via random forest classifiers. We show that the resulting matching volume estimation method achieves similar accuracy to purely data-driven alternatives on benchmarks and that it generalizes to unseen data much better. In fact, the results we submitted to the KITTI and ETH3D benchmarks were generated using a classifier trained on the Middlebury 2014 dataset.