Deep Fence Estimation using Stereo Guidance and Adversarial Learning
This work addresses the challenge of fence removal in photography, which is important for photographers and image processing applications, but it appears incremental as it builds on existing segmentation techniques with specific enhancements.
The paper tackles the problem of accurately segmenting fences in images, which is crucial for removing occlusions, by introducing a fence guidance mask from stereo images and a directional connectivity loss, achieving superior performance over state-of-the-art methods.
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.