Spike Depth Estimation

Spike depth estimation focuses on accurately determining distances in scenes using data from neuromorphic spike cameras, which offer advantages over traditional cameras in high-velocity scenarios. Current research emphasizes unsupervised learning methods, often employing recurrent spiking neural networks (RSNNs) with heterogeneous neuronal and synaptic dynamics, or leveraging cross-modality knowledge transfer from RGB data to improve the robustness and accuracy of depth maps generated from sparse spike data. This field is significant for advancing computer vision applications like autonomous driving, particularly where high temporal resolution and motion robustness are crucial.