  Monocular depth estimation is a challenging problem on which deep neural
networks have demonstrated great potential. However, depth maps predicted by
existing deep models usually lack fine-grained details due to the convolution
operations and the down-samplings in networks. We find that increasing input
resolution is helpful to preserve more local details while the estimation at
low resolution is more accurate globally. Therefore, we propose a novel depth
map fusion module to combine the advantages of estimations with
multi-resolution inputs. Instead of merging the low- and high-resolution
estimations equally, we adopt the core idea of Poisson fusion, trying to
implant the gradient domain of high-resolution depth into the low-resolution
depth. While classic Poisson fusion requires a fusion mask as supervision, we
propose a self-supervised framework based on guided image filtering. We
demonstrate that this gradient-based composition performs much better at noisy
immunity, compared with the state-of-the-art depth map fusion method. Our
lightweight depth fusion is one-shot and runs in real-time, making our method
80X faster than a state-of-the-art depth fusion method. Quantitative
evaluations demonstrate that the proposed method can be integrated into many
fully convolutional monocular depth estimation backbones with a significant
performance boost, leading to state-of-the-art results of detail enhancement on
depth maps.
