  Deep learning has improved vanishing point detection in images. Yet, deep
networks require expensive annotated datasets trained on costly hardware and do
not generalize to even slightly different domains, and minor problem variants.
Here, we address these issues by injecting deep vanishing point detection
networks with prior knowledge. This prior knowledge no longer needs to be
learned from data, saving valuable annotation efforts and compute, unlocking
realistic few-sample scenarios, and reducing the impact of domain changes.
Moreover, the interpretability of the priors allows to adapt deep networks to
minor problem variations such as switching between Manhattan and non-Manhattan
worlds. We seamlessly incorporate two geometric priors: (i) Hough Transform --
mapping image pixels to straight lines, and (ii) Gaussian sphere -- mapping
lines to great circles whose intersections denote vanishing points.
Experimentally, we ablate our choices and show comparable accuracy to existing
models in the large-data setting. We validate our model's improved data
efficiency, robustness to domain changes, adaptability to non-Manhattan
settings.
