清华天眸芯再登Nature系列期刊封面：类脑互补视觉范式重塑AI感知

TL;DR - Tsinghua researchers extended the TianMou brain-inspired complementary-vision chip into a self-supervised perception stack for robust imaging in high-speed, low-light, and high-dynamic-range environments. The approach reduces redundant sensor data while improving inputs for physical AI systems.

• TianMou captures complementary RGB, spatial-difference, and temporal-difference signals through cognition- and motion-oriented pathways.
• IGFNet uses cross-path attention, memory, and temporal self-supervision to combine reliable cues and reconstruct missing structure without perfect-image labels.
• Its learned representations transfer to monocular depth estimation, video instance segmentation, and visual odometry under severe visual degradation.
• The team released TianMouCV, an open-source toolkit supporting datasets, reconstruction, and downstream applications.