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Pixel wise image labeling is an interesting and challenging problem withgreat significance in the computer vision community. In order for a denselabeling algorithm to be able to achieve accurate and precise results, it hasto consider the dependencies that exist in the joint space of both the inputand the output variables. An implicit approach for modeling those dependenciesis by training a deep neural network that, given as input an initial estimateof the output labels and the input image, it will be able to predict a newrefined estimate for the labels. In this context, our work is concerned withwhat is the optimal architecture for performing the label improvement task. Weargue that the prior approaches of either directly predicting new labelestimates or predicting residual corrections w.r.t. the initial labels withfeed-forward deep network architectures are sub-optimal. Instead, we propose ageneric architecture that decomposes the label improvement task to three steps:1) detecting the initial label estimates that are incorrect, 2) replacing theincorrect labels with new ones, and finally 3) refining the renewed labels bypredicting residual corrections w.r.t. them. Furthermore, we explore andcompare various other alternative architectures that consist of theaforementioned Detection, Replace, and Refine components. We extensivelyevaluate the examined architectures in the challenging task of dense disparityestimation (stereo matching) and we report both quantitative and qualitativeresults on three different datasets. Finally, our dense disparity estimationnetwork that implements the proposed generic architecture, achievesstate-of-the-art results in the KITTI 2015 test surpassing prior approaches bya significant margin.

Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling