Self-distillation framework for indoor and outdoor monocular depth estimation

As one of the most crucial tasks of scene perception, Monocular Depth Estimation (MDE) has made considerable development in recent years. Current MDE researchers are interested in the precision and speed of the estimation, but pay less attention to the generalization ability across scenes. For instance, the MDE networks trained on outdoor scenes achieve impressive performance on outdoor scenes but poor performance on indoor scenes, and vice versa. To tackle this problem, we propose a self-distillation MDE framework to improve the generalization ability across different scenes in this paper. Specifically, we design a student encoder that extracts features from two datasets of indoor and outdoor scenes, respectively. After that, we introduce a dissimilarity loss to pull apart encoded features of different scenes in the feature space. Finally, a decoder is adopted to estimate the final depth from encoded features. By doing so, our self-distillation MDE framework can learn the depth estimation of two different datasets. To the best of our knowledge, we are the first one to tackle the generalization problem across datasets of different scenes in the MDE field. Experiments demonstrate that our method reduces the degradation problem when a MDE network is in the face of datasets with complex data distribution. Note that evaluating on two datasets by a single network is more challenging than evaluating on two datasets by two different networks.