# Left-Right Comparative Recurrent Model for Stereo Matching

arXiv: https://arxiv.org/abs/1804.00796 · HTML: https://mlsanity.com/paper/1804.00796/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 12 of 12)

- [Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans](https://mlsanity.com/paper/2110.04994/) (2021)
- [CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow](https://mlsanity.com/paper/2211.10408/) (2022)
- [Neural Disparity Refinement for Arbitrary Resolution Stereo](https://mlsanity.com/paper/2110.15367/) (2021)
- [ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images](https://mlsanity.com/paper/2106.08107/) (2021)
- [WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images](https://mlsanity.com/paper/2206.02342/) (2022)
- [RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation](https://mlsanity.com/paper/2206.07047/) (2022)
- [LTRL: Boosting Long-tail Recognition via Reflective Learning](https://mlsanity.com/paper/2407.12568/) (2024)
- [ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images](https://mlsanity.com/paper/2103.07798/) (2021)
- [L3C-Stereo: Lossless Compression for Stereo Images](https://mlsanity.com/paper/2108.09422/) (2021)
- [Left-right Discrepancy for Adversarial Attack on Stereo Networks](https://mlsanity.com/paper/2401.07188/) (2024)
- [Bayesian Learning for Disparity Map Refinement for Semi-Dense Active Stereo Vision](https://mlsanity.com/paper/2209.05082/) (2022)
- [A Lightweight Target-Driven Network of Stereo Matching for Inland Waterways](https://mlsanity.com/paper/2410.07915/) (2024)

## Abstract (arXiv)

Leveraging the disparity information from both left and right views is crucial for stereo disparity estimation. Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view. However, the conventional left-right consistency check is an isolated post-processing step and heavily hand-crafted. This paper proposes a novel left-right comparative recurrent model to perform left-right consistency checking jointly with disparity estimation. At each recurrent step, the model produces disparity results for both views, and then performs online left-right comparison to identify the mismatched regions which may probably contain erroneously labeled pixels. A soft attention mechanism is introduced, which employs the learned error maps for better guiding the model to selectively focus on refining the unreliable regions at the next recurrent step. In this way, the generated disparity maps are progressively improved by the proposed recurrent model. Extensive evaluations on KITTI 2015, Scene Flow and Middlebury benchmarks validate the effectiveness of our model, demonstrating that state-of-the-art stereo disparity estimation results can be achieved by this new model.