Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation,depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network. Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks, the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both. Experimental results demonstrate that HybridNet results are comparable with the state of the art methods, as well as the single task methods that HybridNet is based on.

翻译：语义分割与深度估计是图像处理领域的两项重要任务。传统上，这两项任务以独立方式处理。然而，在需要几何与语义信息的应用场景中（如机器人或自主导航），单一的深度估计或语义分割均不足以满足需求。本文提出通过一种混合卷积网络，从单张输入图像中联合处理深度估计与语义分割任务。与现有方法中为两项任务采用单一特征提取网络的做法不同，所提出的HybridNet通过分离特定任务相关特征与共享相关特征，改进了特征提取过程。实验结果表明，HybridNet的性能与现有最优方法相当，同时也与其所基于的单任务方法具有可比性。