  People capture memorable images of events and exhibits that are often
occluded by a wire mesh loosely termed as fence. Recent works in removing fence
have limited performance due to the difficulty in initial fence segmentation.
This work aims to accurately segment fence using a novel fence guidance mask
(FM) generated from stereo image pair. This binary guidance mask contains
deterministic cues about the structure of fence and is given as additional
input to the deep fence estimation model. We also introduce a directional
connectivity loss (DCL), which is used alongside adversarial loss to precisely
detect thin wires. Experimental results obtained on real world scenarios
demonstrate the superiority of proposed method over state-of-the-art
techniques.
