  Most existing methods often rely on complex models to predict scene depth
with high accuracy, resulting in slow inference that is not conducive to
deployment. To better balance precision and speed, we first designed SmallDepth
based on sparsity. Second, to enhance the feature representation ability of
SmallDepth during training under the condition of equal complexity during
inference, we propose an equivalent transformation module(ETM). Third, to
improve the ability of each layer in the case of a fixed SmallDepth to perceive
different context information and improve the robustness of SmallDepth to the
left-right direction and illumination changes, we propose pyramid loss. Fourth,
to further improve the accuracy of SmallDepth, we utilized the proposed
function approximation loss (APX) to transfer knowledge in the pretrained
HQDecv2, obtained by optimizing the previous HQDec to address grid artifacts in
some regions, to SmallDepth. Extensive experiments demonstrate that each
proposed component improves the precision of SmallDepth without changing the
complexity of SmallDepth during inference, and the developed approach achieves
state-of-the-art results on KITTI at an inference speed of more than 500 frames
per second and with approximately 2 M parameters. The code and models will be
publicly available at https://github.com/fwucas/FA-Depth.
