  State-of-the-art self-supervised learning approaches for monocular depth
estimation usually suffer from scale ambiguity. They do not generalize well
when applied on distance estimation for complex projection models such as in
fisheye and omnidirectional cameras. This paper introduces a novel multi-task
learning strategy to improve self-supervised monocular distance estimation on
fisheye and pinhole camera images. Our contribution to this work is threefold:
Firstly, we introduce a novel distance estimation network architecture using a
self-attention based encoder coupled with robust semantic feature guidance to
the decoder that can be trained in a one-stage fashion. Secondly, we integrate
a generalized robust loss function, which improves performance significantly
while removing the need for hyperparameter tuning with the reprojection loss.
Finally, we reduce the artifacts caused by dynamic objects violating static
world assumptions using a semantic masking strategy. We significantly improve
upon the RMSE of previous work on fisheye by 25% reduction in RMSE. As there is
little work on fisheye cameras, we evaluated the proposed method on KITTI using
a pinhole model. We achieved state-of-the-art performance among self-supervised
methods without requiring an external scale estimation.
