  While road obstacle detection techniques have become increasingly effective,
they typically ignore the fact that, in practice, the apparent size of the
obstacles decreases as their distance to the vehicle increases. In this paper,
we account for this by computing a scale map encoding the apparent size of a
hypothetical object at every image location. We then leverage this perspective
map to (i) generate training data by injecting onto the road synthetic objects
whose size corresponds to the perspective foreshortening; and (ii) incorporate
perspective information in the decoding part of the detection network to guide
the obstacle detector. Our results on standard benchmarks show that, together,
these two strategies significantly boost the obstacle detection performance,
allowing our approach to consistently outperform state-of-the-art methods in
terms of instance-level obstacle detection.
