Abstract. Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multitask architectures or computationally expensive graphical models. We present a method that leverages a fully convolutional network (FCN) to predict semantic labels, depth and an instance-based encoding using each pixel's direction towards its corresponding instance center. Subsequently, we apply low-level computer vision techniques to generate stateof-the-art instance segmentation on the street scene datasets KITTI and Cityscapes. Our approach outperforms existing works by a large margin and can additionally predict absolute distances of individual instances from a monocular image as well as a pixel-level semantic labeling.
In particular, REF trains a network to predict each pixel direction towards its instance center along with monocular depth and semantic labeling.
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
ArXiv
ArXiv
Computer Science