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Current methods for depth map prediction from monocular images tend topredict smooth, poorly localized contours for the occlusion boundaries in theinput image. This is unfortunate as occlusion boundaries are important cues torecognize objects, and as we show, may lead to a way to discover new objectsfrom scene reconstruction. To improve predicted depth maps, recent methods relyon various forms of filtering or predict an additive residual depth map torefine a first estimate. We instead learn to predict, given a depth mappredicted by some reconstruction method, a 2D displacement field able tore-sample pixels around the occlusion boundaries into sharper reconstructions.Our method can be applied to the output of any depth estimation method, in anend-to-end trainable fashion. For evaluation, we manually annotated theocclusion boundaries in all the images in the test split of popular NYUv2-Depthdataset. We show that our approach improves the localization of occlusionboundaries for all state-of-the-art monocular depth estimation methods that wecould evaluate, without degrading the depth accuracy for the rest of theimages.

Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields