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In this work, we demonstrate yet another approach to tackle the amodalsegmentation problem. Specifically, we first introduce a new representation,namely a semantics-aware distance map (sem-dist map), to serve as our targetfor amodal segmentation instead of the commonly used masks and heatmaps. Thesem-dist map is a kind of level-set representation, of which the differentregions of an object are placed into different levels on the map according totheir visibility. It is a natural extension of masks and heatmaps, where modal,amodal segmentation, as well as depth order information, are allwell-described. Then we also introduce a novel convolutional neural network(CNN) architecture, which we refer to as semantic layering network, to estimatesem-dist maps layer by layer, from the global-level to the instance-level, forall objects in an image. Extensive experiments on the COCOA and D2SA datasetshave demonstrated that our framework can predict amodal segmentation, occlusionand depth order with state-of-the-art performance.

Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation