AI RESEARCH
SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams
arXiv CS.CV
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ArXi:2505.19487v3 Announce Type: replace Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asynchronous events at microsecond-level resolution, providing an alternative sensing modality. However, existing methods lack specialized stereo algorithms and benchmarks tailored to the spike data. To address this gap, we propose SpikeStereoNet, a brain-inspired framework and the first to estimate stereo depth directly from raw spike streams.