  Learning depth from spherical panoramas is becoming a popular research topic
because a panorama has a full field-of-view of the environment and provides a
relatively complete description of a scene. However, applying well-studied CNNs
for perspective images to the standard representation of spherical panoramas,
i.e., the equirectangular projection, is suboptimal, as it becomes distorted
towards the poles. Another representation is the cubemap projection, which is
distortion-free but discontinued on edges and limited in the field-of-view.
This paper introduces a new framework to fuse features from the two
projections, unidirectionally feeding the cubemap features to the
equirectangular features only at the decoding stage. Unlike the recent
bidirectional fusion approach operating at both the encoding and decoding
stages, our fusion scheme is much more efficient. Besides, we also designed a
more effective fusion module for our fusion scheme. Experiments verify the
effectiveness of our proposed fusion strategy and module, and our model
achieves state-of-the-art performance on four popular datasets. Additional
experiments show that our model also has the advantages of model complexity and
generalization capability.
