FutureDepth: Learning to Predict the Future Improves Video Depth Estimation

TL;DR

FutureDepth introduces a novel approach to video depth estimation by learning to predict future frames, leveraging multi-frame and motion cues, resulting in state-of-the-art accuracy across diverse benchmarks.

Contribution

The paper proposes FutureDepth, a new method that incorporates future prediction and multi-frame correspondence learning to enhance video depth estimation performance.

Findings

Significantly outperforms existing methods on NYUDv2, KITTI, DDAD, and Sintel benchmarks.

Achieves state-of-the-art accuracy in video depth estimation.

Maintains efficiency comparable to monocular models.

Abstract

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