AI RESEARCH
Non-Learning Low-Light Stereo Vision
arXiv CS.CV
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ArXi:2606.00379v1 Announce Type: new We present a non-learning stereo framework for disparity estimation from severely noisy images. Using the Field of Junctions (FoJ), it retains coarse visual features stable under severe noise for cost volume construction while discarding fine textures inseparable from photon noise. The resulting structural information guides boundary-aware Semi-Global Matching (SGM) that dynamically adapts smoothness penalties to preserve true disparity discontinuities.