MLG-Stereo adds multi-granularity feature extraction, local-global cost volumes, and guided recurrent refinement to ViT stereo matching, yielding competitive results on Middlebury, KITTI-2015, and strong results on KITTI-2012.
Cfnet: Cascade and fused cost volume for robust stereo matching,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GREATEN fuses surface normals with image features via gated contextual-geometric fusion and efficient sparse attentions to cut stereo matching errors by up to 30% on real datasets when trained solely on synthetic data.
citing papers explorer
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MLG-Stereo: ViT Based Stereo Matching with Multi-Stage Local-Global Enhancement
MLG-Stereo adds multi-granularity feature extraction, local-global cost volumes, and guided recurrent refinement to ViT stereo matching, yielding competitive results on Middlebury, KITTI-2015, and strong results on KITTI-2012.
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Geometry Reinforced Efficient Attention Tuning Equipped with Normals for Robust Stereo Matching
GREATEN fuses surface normals with image features via gated contextual-geometric fusion and efficient sparse attentions to cut stereo matching errors by up to 30% on real datasets when trained solely on synthetic data.