  We present a novel method for predicting accurate depths from monocular
images with high efficiency. This optimal efficiency is achieved by exploiting
wavelet decomposition, which is integrated in a fully differentiable
encoder-decoder architecture. We demonstrate that we can reconstruct
high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast
with previous works, we show that wavelet coefficients can be learned without
direct supervision on coefficients. Instead we supervise only the final depth
image that is reconstructed through the inverse wavelet transform. We
additionally show that wavelet coefficients can be learned in fully
self-supervised scenarios, without access to ground-truth depth. Finally, we
apply our method to different state-of-the-art monocular depth estimation
models, in each case giving similar or better results compared to the original
model, while requiring less than half the multiply-adds in the decoder network.
Code at https://github.com/nianticlabs/wavelet-monodepth
