  In this paper, we propose a novel video depth estimation approach,
FutureDepth, which enables the model to implicitly leverage multi-frame and
motion cues to improve depth estimation by making it learn to predict the
future at training. More specifically, we propose a future prediction network,
F-Net, which takes the features of multiple consecutive frames and is trained
to predict multi-frame features one time step ahead iteratively. In this way,
F-Net learns the underlying motion and correspondence information, and we
incorporate its features into the depth decoding process. Additionally, to
enrich the learning of multiframe correspondence cues, we further leverage a
reconstruction network, R-Net, which is trained via adaptively masked
auto-encoding of multiframe feature volumes. At inference time, both F-Net and
R-Net are used to produce queries to work with the depth decoder, as well as a
final refinement network. Through extensive experiments on several benchmarks,
i.e., NYUDv2, KITTI, DDAD, and Sintel, which cover indoor, driving, and
open-domain scenarios, we show that FutureDepth significantly improves upon
baseline models, outperforms existing video depth estimation methods, and sets
new state-of-the-art (SOTA) accuracy. Furthermore, FutureDepth is more
efficient than existing SOTA video depth estimation models and has similar
latencies when comparing to monocular models
