arXiv Computer Vision
PhasorNet: Learning Structure from Frequency for Real-Time Stereo Matching
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Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth
arXiv:2607.15600v2 Announce Type: replace Abstract: Monocular depth foundation models have demonstrated remarkable generalization capabilities across diverse environments. However, they continue to s...
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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...
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StereoDiffuer: Diffusion-based Progressive Geometry Modeling with Saliency Attention Perception for Stereo Matching
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M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo
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WAVE-Stereo: Warp-Aligned Volume Encoding for 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.