  Model generalizability to unseen datasets, concerned with in-the-wild
robustness, is less studied for indoor single-image depth prediction. We
leverage gradient-based meta-learning for higher generalizability on zero-shot
cross-dataset inference. Unlike the most-studied image classification in
meta-learning, depth is pixel-level continuous range values, and mappings from
each image to depth vary widely across environments. Thus no explicit task
boundaries exist. We instead propose fine-grained task that treats each RGB-D
pair as a task in our meta-optimization. We first show meta-learning on limited
data induces much better prior (max +29.4\%). Using meta-learned weights as
initialization for following supervised learning, without involving extra data
or information, it consistently outperforms baselines without the method.
Compared to most indoor-depth methods that only train/ test on a single
dataset, we propose zero-shot cross-dataset protocols, closely evaluate
robustness, and show consistently higher generalizability and accuracy by our
meta-initialization. The work at the intersection of depth and meta-learning
potentially drives both research streams to step closer to practical use.
