Hybridnet for depth estimation and semantic segmentation
This addresses the need for combined geometric and semantic information in applications like robotics and autonomous navigation, but it is incremental as it builds on existing methods.
The paper tackles the joint problem of depth estimation and semantic segmentation from a single image by proposing HybridNet, which separates task-specific features from shared ones, achieving results comparable to state-of-the-art and single-task methods.
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.