  Recently, Depth Anything Model (DAM) - a type of depth foundation model -
reveals impressive zero-shot capacity for diverse perspective images. Despite
its success, it remains an open question regarding DAM's performance on 360
images that enjoy a large field-of-view (180x360) but suffer from spherical
distortions. To this end, we establish, to our knowledge, the first benchmark
that aims to 1) evaluate the performance of DAM on 360 images and 2) develop a
powerful 360 DAM for the benefit of the community. For this, we conduct a large
suite of experiments that consider the key properties of 360 images, e.g.,
different 360 representations, various spatial transformations, and diverse
indoor and outdoor scenes. This way, our benchmark unveils some key findings,
e.g., DAM is less effective for diverse 360 scenes and sensitive to spatial
transformations. To address these challenges, we first collect a large-scale
unlabeled dataset including diverse indoor and outdoor scenes. We then propose
a semi-supervised learning (SSL) framework to learn a 360 DAM, dubbed Any360D.
Under the umbrella of SSL, Any360D first learns a teacher model by fine-tuning
DAM via metric depth supervision. Then, we train the student model by
uncovering the potential of large-scale unlabeled data with pseudo labels from
the teacher model. M\"obius transformation-based spatial augmentation (MTSA) is
proposed to impose consistency regularization between the unlabeled data and
spatially transformed ones. This subtly improves the student model's robustness
to various spatial transformations even under severe distortions. Extensive
experiments demonstrate that Any360D outperforms DAM and many prior
data-specific models, e.g., PanoFormer, across diverse scenes, showing
impressive zero-shot capacity for being a 360 depth foundation model.
