  Due to the rise of spherical cameras, monocular 360 depth estimation becomes
an important technique for many applications (e.g., autonomous systems). Thus,
state-of-the-art frameworks for monocular 360 depth estimation such as
bi-projection fusion in BiFuse are proposed. To train such a framework, a large
number of panoramas along with the corresponding depth ground truths captured
by laser sensors are required, which highly increases the cost of data
collection. Moreover, since such a data collection procedure is time-consuming,
the scalability of extending these methods to different scenes becomes a
challenge. To this end, self-training a network for monocular depth estimation
from 360 videos is one way to alleviate this issue. However, there are no
existing frameworks that incorporate bi-projection fusion into the
self-training scheme, which highly limits the self-supervised performance since
bi-projection fusion can leverage information from different projection types.
In this paper, we propose BiFuse++ to explore the combination of bi-projection
fusion and the self-training scenario. To be specific, we propose a new fusion
module and Contrast-Aware Photometric Loss to improve the performance of BiFuse
and increase the stability of self-training on real-world videos. We conduct
both supervised and self-supervised experiments on benchmark datasets and
achieve state-of-the-art performance.
