FutureDepth: Learning to Predict the Future Improves Video Depth
  Estimation
Rajeev Yasarla Manish Kumar Singh Hong Cai Yunxiao Shi Jisoo Jeong Yinhao Zhu Shizhong Han Risheek Garrepalli Fatih Porikli
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