  Self-supervised deep learning methods have leveraged stereo images for
training monocular depth estimation. Although these methods show strong results
on outdoor datasets such as KITTI, they do not match performance of supervised
methods on indoor environments with camera rotation. Indoor, rotated scenes are
common for less constrained applications and pose problems for two reasons:
abundance of low texture regions and increased complexity of depth cues for
images under rotation. In an effort to extend self-supervised learning to more
generalised environments we propose two additions. First, we propose a novel
Filled Disparity Loss term that corrects for ambiguity of image reconstruction
error loss in textureless regions. Specifically, we interpolate disparity in
untextured regions, using the estimated disparity from surrounding textured
areas, and use L1 loss to correct the original estimation. Our experiments show
that depth estimation is substantially improved on low-texture scenes, without
any loss on textured scenes, when compared to Monodepth by Godard et al.
Secondly, we show that training with an application's representative rotations,
in both pitch and roll, is sufficient to significantly improve performance over
the entire range of expected rotation. We demonstrate that depth estimation is
successfully generalised as performance is not lost when evaluated on test sets
with no camera rotation. Together these developments enable a broader use of
self-supervised learning of monocular depth estimation for complex
environments.
