arXiv:2608.29819v1 Announce Type: new Abstract: Accurate stereo matching remains challenging in ill-posed regions such as fine structures, reflective, or transparent objects, where appearance cues ar...
arXiv:2508.10838v3 Announce Type: replace Abstract: Learning disparity in occluded regions remains difficult for unsupervised stereo matching. Photometric supervision lacks valid target-view correspo...
E3Stereo improves stereo matching at depth boundaries by learning geometric edges from disparity supervision. It integrates these edges with backbone disparity and image details to predict full‑resolution disparity corrections, emphasizing boundary errors during training. On the Scene Flow dataset, E3Stereo reduces end‑point error at depth boundaries by 9.5%–14.3% across multiple models and improves all eleven metrics on ETH3D, KITTI 2012, and KITTI 2015 after fine‑tuning.
arXiv:2608.08685v2 Announce Type: replace Abstract: Reliable semi-dense matching is essential for modern geometric vision systems. Designed under a coarse-to-fine paradigm, it achieves an optimal bal...
arXiv:2608.20788v1 Announce Type: new Abstract: Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or...
The paper introduces Epipolar Distillation (EpiDistill), a method that transfers scale‑aware geometric priors from multi‑view models to monocular depth foundation models using Rectified Stereo Tokens. By preserving epipolar attention patterns, the single‑view model maintains geometric consistency without needing multi‑view inputs during inference. Experiments show significant improvements in zero‑shot metric depth estimation on challenging datasets such as ETH3D and DIODE, and the approach consistently boosts performance of state‑of‑the‑art ViT‑based models like UniDepthV2 and DepthPro.
Learning-based stereo matching models struggle in underwater environments due to scarce in-domain data and the difficulty of extracting discriminative correspondences from degraded imagery. In this work, we present $\textbf{AquaStereo}$, a perception-enhanced framework with a data simulation pipeline and a self-distillation strategy that jointly address data scarcity and feature degradation in underwater stereo matching.
Existing iterative stereo matching methods primarily adopt two types of correspondence representation: explicit matching search via correlation volumes and local residual refinement via warped features, yet the two remain separately modeled. We propose WAVE-Stereo, built on a core insight: correlation volumes and feature warping provide complementary matching cues.
arXiv:2609.38592v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) enables reconstruction without per- scene optimisation, but practical stereo-camera applications require near...
Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a deterministic regression problem, collapsing the multimodal distribution modeling into a single-point estimation.
arXiv:2608.28933v1 Announce Type: new Abstract: Stereo reconstruction is one of the last remaining Computer Vision tasks where all state-of-the-art methods employ a heavy architectural inductive bias...
SFVO is a stereo visual‑odometry framework that leverages pretrained stereo‑matching and optical‑flow models to obtain dense stereo and temporal correspondences. Rather than learning pose directly from images, it maps these correspondences into geometric constraints and predicts trustworthy points using decoupled confidence maps for rotation and translation. Experiments on both outdoor and indoor datasets show that SFVO delivers robust, accurate pose estimation with strong generalization, and the authors plan to release the code.