How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting

TL;DR

This paper introduces a self-supervised, deterministic method for predicting future monocular depth in videos, avoiding reliance on large annotated datasets and probabilistic models, by framing depth forecasting as a view synthesis task.

Contribution

It proposes DeFNet, a novel depth forecasting network, and a channel-attention pose estimator, enabling cost-effective, accurate future depth prediction without ground truth depth data.

Findings

Outperforms state-of-the-art in Abs Rel metric on KITTI and Cityscapes.

Effective for both short-term and mid-term depth forecasting.

Demonstrates practical self-supervised depth prediction for unobserved video frames.

Abstract

In this paper we present a novel self-supervised method to anticipate the depth estimate for a future, unobserved real-world urban scene. This work is the first to explore self-supervised learning for estimation of monocular depth of future unobserved frames of a video. Existing works rely on a large number of annotated samples to generate the probabilistic prediction of depth for unseen frames. However, this makes it unrealistic due to its requirement for large amount of annotated depth samples of video. In addition, the probabilistic nature of the case, where one past can have multiple future outcomes often leads to incorrect depth estimates. Unlike previous methods, we model the depth estimation of the unobserved frame as a view-synthesis problem, which treats the depth estimate of the unseen video frame as an auxiliary task while synthesizing back the views using learned pose. This approach is not only cost effective - we do not use any ground truth depth for training (hence practical) but also deterministic (a sequence of past frames map to an immediate future). To address this task we first develop a novel depth forecasting network DeFNet which estimates depth of unobserved future by forecasting latent features. Second, we develop a channel-attention based pose estimation network that estimates the pose of the unobserved frame. Using this learned pose, estimated depth map is reconstructed back into the image domain, thus forming a self-supervised solution. Our proposed approach shows significant improvements in Abs Rel metric compared to state-of-the-art alternatives on both short and mid-term forecasting setting, benchmarked on KITTI and Cityscapes. Code is available at https://github.com/sauradip/depthForecasting