LinStereo uses Position-Aware Linear Attention, Hierarchical Semantic Cost Volumes, and Depth Prior Initialization to enable global aggregation in iterative stereo matching at linear complexity, showing improved performance on standard and underwater benchmarks.
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High-resolution stereo datasets with subpixel-accurate ground truth
2 Pith papers cite this work, alongside 1,297 external citations. Polarity classification is still indexing.
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StereoGenBench is a new synthetic benchmark dataset featuring calibrated multi-baseline stereo pairs with dense metric depth, intrinsics, and poses from Unreal Engine renders for controlled evaluation of stereo generation.
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LinStereo: Linear-Complexity Global Attention for Multi-Scale Iterative Stereo Matching
LinStereo uses Position-Aware Linear Attention, Hierarchical Semantic Cost Volumes, and Depth Prior Initialization to enable global aggregation in iterative stereo matching at linear complexity, showing improved performance on standard and underwater benchmarks.
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StereoGenBench: A Synthetic Multi-Camera Benchmark for Stereo Generation under Controlled Baseline Regimes
StereoGenBench is a new synthetic benchmark dataset featuring calibrated multi-baseline stereo pairs with dense metric depth, intrinsics, and poses from Unreal Engine renders for controlled evaluation of stereo generation.