arXiv Computer Vision
PhasorNet: Learning Structure from Frequency for Real-Time Stereo Matching
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arXiv Computer Vision
Sep 3
Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth
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.
Hugging Face Trending Papers
Aug 4
SGFormer: Structure-Guided Transformer for Robust Local Feature Matching
Local feature matching is a fundamental component of photogrammetry, enabling accurate image correspondence critical for tasks such as 3D reconstruction, stereo mapping, and visual localization. While recent detector-free matching methods, like LoFTR, have advanced the field, the global features obtained by leveraging the global-range modeling capacity of the unconstrained attention mechanism compromise the model's attention to the salient structures in certain scenarios.
arXiv Computer Vision
Sep 1
SGFormer: Structure-Guided Transformer for Robust Local Feature Matching
arXiv:2608.03423v2 Announce Type: replace Abstract: Local feature matching is a fundamental component of photogrammetry, enabling accurate image correspondence critical for tasks such as 3D reconstru...
arXiv Computer Vision
Aug 25
StereoDiffuer: Diffusion-based Progressive Geometry Modeling with Saliency Attention Perception for Stereo Matching
arXiv:2608.21710v1 Announce Type: new Abstract: With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stere...
arXiv Computer Vision
Aug 24
M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo
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...
arXiv AI
1d ago
E3tereo: Improving Stereo Matching at Depth Boundaries with Learned Geometric Edges
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.