Abstract

An algorithm to combine multiple loss terms adaptively for training a monocular depth estimator is proposed in this work. We construct a loss function space containing tens of losses. Using more losses can improve inference capability without any additional complexity in the test phase. However, when many losses are used, some of them may be neglected during training. Also, since each loss decreases at a different speed, adaptive weighting is required to balance the contributions of the losses. To address these issues, we propose the loss rebalancing algorithm that initializes and rebalances the weight for each loss function adaptively in the course of training. Experimental results show that the proposed algorithm provides state-of-the-art depth estimation results on various datasets.