  Neuromorphic spike data, an upcoming modality with high temporal resolution,
has shown promising potential in autonomous driving by mitigating the
challenges posed by high-velocity motion blur. However, training the spike
depth estimation network holds significant challenges in two aspects: sparse
spatial information for pixel-wise tasks and difficulties in achieving paired
depth labels for temporally intensive spike streams. Therefore, we introduce
open-source RGB data to support spike depth estimation, leveraging its
annotations and spatial information. The inherent differences in modalities and
data distribution make it challenging to directly apply transfer learning from
open-source RGB to target spike data. To this end, we propose a cross-modality
cross-domain (BiCross) framework to realize unsupervised spike depth estimation
by introducing simulated mediate source spike data. Specifically, we design a
Coarse-to-Fine Knowledge Distillation (CFKD) approach to facilitate
comprehensive cross-modality knowledge transfer while preserving the unique
strengths of both modalities, utilizing a spike-oriented uncertainty scheme.
Then, we propose a Self-Correcting Teacher-Student (SCTS) mechanism to screen
out reliable pixel-wise pseudo labels and ease the domain shift of the student
model, which avoids error accumulation in target spike data. To verify the
effectiveness of BiCross, we conduct extensive experiments on four scenarios,
including Synthetic to Real, Extreme Weather, Scene Changing, and Real Spike.
Our method achieves state-of-the-art (SOTA) performances, compared with
RGB-oriented unsupervised depth estimation methods. Code and dataset:
https://github.com/Theia-4869/BiCross
