Introduces the TUB dataset of 1320 real turbid underwater images and PCD metric showing strong correlation with instance segmentation performance where standard metrics fail.
In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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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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Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
Introduces the TUB dataset of 1320 real turbid underwater images and PCD metric showing strong correlation with instance segmentation performance where standard metrics fail.
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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.