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We present a novel method for predicting accurate depths from monocularimages with high efficiency. This optimal efficiency is achieved by exploitingwavelet decomposition, which is integrated in a fully differentiableencoder-decoder architecture. We demonstrate that we can reconstructhigh-fidelity depth maps by predicting sparse wavelet coefficients. In contrastwith previous works, we show that wavelet coefficients can be learned withoutdirect supervision on coefficients. Instead we supervise only the final depthimage that is reconstructed through the inverse wavelet transform. Weadditionally show that wavelet coefficients can be learned in fullyself-supervised scenarios, without access to ground-truth depth. Finally, weapply our method to different state-of-the-art monocular depth estimationmodels, in each case giving similar or better results compared to the originalmodel, while requiring less than half the multiply-adds in the decoder network.Code at https://github.com/nianticlabs/wavelet-monodepth

Single Image Depth Prediction with Wavelet Decomposition