LAUSR

Current top-performing coarse-to-fine monocular depth estimation systems mainly depend on deeper backbones, such as a full ResNet50. These systems benefit from the powerful multiscale feature representations but suffer from the high computational costs and memory overheads. Conversely, the recurrent depth refinement systems fulfill the lightweight architecture, while the local multiscale context often limits their accuracies. To handle this problem, we propose an atrous spatial (AS) module by utilizing atrous convolutions with different rates, which efficiently captures the multiscale context. We further assemble our AS module to the current recurrent depth refinement system R-MSFM, constructing a powerful monocular depth estimation system called RAFM. To evaluate its effectiveness, we conduct the comprehensive experiments and show that our RAFM outperforms the previous SOTA methods in all metrics on KITTI while keeping the real-time speed. For accuracy, our RAFM achieves a square relative error (Sq Rel) of 0.702 on KITTI, a 12.6% error reduction from the previous SOTA method (0.802). For model size and inference speed, our RAFM gets a frame rate of 33fps on a GPU with 7.5 M parameters, which is faster and more lightweight than the recent SOTA method (5fps and 128 M).