  Current stereo matching techniques are challenged by restricted searching
space, occluded regions and sheer size. While single image depth estimation is
spared from these challenges and can achieve satisfactory results with the
extracted monocular cues, the lack of stereoscopic relationship renders the
monocular prediction less reliable on its own, especially in highly dynamic or
cluttered environments. To address these issues in both scenarios, we present
an optic-chiasm-inspired self-supervised binocular depth estimation method,
wherein a vision transformer (ViT) with gated positional cross-attention (GPCA)
layers is designed to enable feature-sensitive pattern retrieval between views
while retaining the extensive context information aggregated through
self-attentions. Monocular cues from a single view are thereafter conditionally
rectified by a blending layer with the retrieved pattern pairs. This crossover
design is biologically analogous to the optic-chasma structure in the human
visual system and hence the name, ChiTransformer. Our experiments show that
this architecture yields substantial improvements over state-of-the-art
self-supervised stereo approaches by 11%, and can be used on both rectilinear
and non-rectilinear (e.g., fisheye) images. Project is available at
https://github.com/ISL-CV/ChiTransformer.
