Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV
Jaime Spencer Chris Russell Simon Hadfield Richard Bowden
Self-supervised monocular depth estimation (SS-MDE) has the potential to
scale to vast quantities of data. Unfortunately, existing approaches limit
themselves to the automotive domain, resulting in models incapable of
generalizing to complex environments such as natural or indoor settings.
  To address this, we propose a large-scale SlowTV dataset curated from
YouTube, containing an order of magnitude more data than existing automotive
datasets. SlowTV contains 1.7M images from a rich diversity of environments,
such as worldwide seasonal hiking, scenic driving and scuba diving. Using this
dataset, we train an SS-MDE model that provides zero-shot generalization to a
large collection of indoor/outdoor datasets. The resulting model outperforms
all existing SSL approaches and closes the gap on supervised SoTA, despite
using a more efficient architecture.
  We additionally introduce a collection of best-practices to further maximize
performance and zero-shot generalization. This includes 1) aspect ratio
augmentation, 2) camera intrinsic estimation, 3) support frame randomization
and 4) flexible motion estimation. Code is available at
https://github.com/jspenmar/slowtv_monodepth.