SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams

Authors: Zhuoheng Gao, Yihao Li, Jiyao Zhang, Rui Zhao, Tong Wu, Hao Tang, Zhaofei Yu, Hao Dong, Guozhang Chen, and Tiejun Huang.

Venue: ICLR 2026 Poster

Abstract:

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. To address the lack of stereo algorithms and benchmarks tailored to spike data, we propose SpikeStereoNet, a brain-inspired framework that estimates stereo depth directly from raw spike streams. The model fuses raw spike streams from two viewpoints and iteratively refines depth through a recurrent spiking neural network update module. We further introduce a large-scale synthetic spike-stream dataset and a real-world stereo spike dataset with dense depth annotations. SpikeStereoNet outperforms existing methods on both datasets while showing strong data efficiency under reduced training data.

Recommended citation: Zhuoheng Gao, Yihao Li, Jiyao Zhang, Rui Zhao, Tong Wu, Hao Tang, Zhaofei Yu, Hao Dong, Guozhang Chen, and Tiejun Huang. (2026). "SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams." ICLR 2026.
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