# Revisiting Matching Response and Swept Feature Volumes for Wide-baseline Omnidirectional Stereo

arXiv: https://arxiv.org/abs/2607.11097 · HTML: https://mlsanity.com/paper/2607.11097/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 19 of 19)

- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [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)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [FoundationStereo: Zero-Shot Stereo Matching](https://mlsanity.com/paper/2501.09898/) (2025)
- [Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching](https://mlsanity.com/paper/1909.03751/) (2019)
- [Normal Assisted Stereo Depth Estimation](https://mlsanity.com/paper/1911.10444/) (2019)
- [Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail](https://mlsanity.com/paper/2412.04472/) (2024)
- [360SD-Net: 360deg Stereo Depth Estimation with Learnable Cost Volume](https://mlsanity.com/paper/1911.04460/) (2019)
- [Real-Time Semantic Stereo Matching](https://mlsanity.com/paper/1910.00541/) (2019)
- [On the confidence of stereo matching in a deep-learning era: a quantitative evaluation](https://mlsanity.com/paper/2101.00431/) (2021)
- [Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation](https://mlsanity.com/paper/2304.00152/) (2023)
- [All-in-One: Transferring Vision Foundation Models into Stereo Matching](https://mlsanity.com/paper/2412.09912/) (2024)
- [360^circ Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron](https://mlsanity.com/paper/2007.06891/) (2020)
- [RomniStereo: Recurrent Omnidirectional Stereo Matching](https://mlsanity.com/paper/2401.04345/) (2024)
- [Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation](https://mlsanity.com/paper/2411.18335/) (2024)
- [Digging Into Normal Incorporated Stereo Matching](https://mlsanity.com/paper/2402.18171/) (2024)
- [Confidence Inference for Focused Learning in Stereo Matching](https://mlsanity.com/paper/1809.09758/) (2018)
- [Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep](https://mlsanity.com/paper/2401.12001/) (2024)

## Abstract (arXiv)

In this paper, we propose a training strategy for confidence estimation in omnidirectional stereo, targeting the ambiguous matches that frequently occur in wide-baseline setups. Reinterpreting the matching responses produced by the 3D encoder decoder block, we show that their expectation values provide intrinsic confidence signals. Building on this, our method directly penalizes ambiguous responses without auxiliary heads, multi-pass inference, or additional modules, resulting in more efficient and generalized predictions. Beyond confidence, we introduce swept feature volume resampling, where response features produced by 3D CNNs are resampled using regressed positive matching indices and then processed by 2D CNNs to predict meta-information such as surface normals. This joint learning introduces auxiliary geometric regularization and improves depth coherence by leveraging additional contextual cues during response aggregation stage. Experimental results demonstrate that our approach enhances both confidence estimation and surface normal prediction while maintaining deployment practicality for autonomous mobility applications.