Joint Depth Estimation and Semantic Segmentation with Adversarial Multi-task Network

摘要

Some exiting works have explored the promotion between depth estimation and semantic segmentation. These works are usually based on convolutional neural networks, which extract compact features and map the relationship between input and output according to specific tasks. In this paper, we introduce a novel adversarial training strategy, that is, generator produces the semantic segmentation map and depth map, and then the discriminator can judge the authenticity of the synthesized color image, thereby supervising the output of the network during back propagation. We use the adversarial loss combined with the reconstruction loss function to supervise the model, and find that the adversarial loss function which is seen as a global supervision can further optimize the output. We use 200 color images from Kitti dataset with semantic segmentation ground truth as the training set, and train the network in an end-to-end manner. The experimental results show that the adversarial training method is well applied to the multi-task training combining semantic segmentation and depth estimation, and can further improve the quantitative performance of depth estimation.