Domain Shared and Specific Prompt Learning for Incremental Monocular Depth Estimation

Incremental monocular depth estimation aims to continuously learn from new domains while maintaining their performance on old domains. The catastrophic forgetting problem is the key challenge when the model adapts the dynamic scene variations. Previous methods usually address this forgetting problem by storing raw samples from the old domain, allowing the model to review the knowledge of the old domain. However, due to the concerns of data privacy and security, our objective is to tackle the incremental monocular depth estimation problem in more stringent scenarios without the need for replaying samples. In this paper, we attribute the cross-domain catastrophic forgetting to the domain distribution shifts and continuous variations of depth space. To this end, we propose Domain Shared and Specific Prompt Learning (DSSP) for incremental monocular depth estimation. In detail, to alleviate the domain distribution shift, complementary domain prompt is designed to learn the domain-shared and domain-specific knowledge which are optimized by the inter-domain alignment and intra-domain orthogonal loss. To mitigate the depth space variations, we first introduce a pre-trained model to generate the domain-shared depth space. Then, we design S^2-Adapter that quantizes depth space variations with scale&shift matrices and converts the domain-shared depth space to domain-specific depth space. Our method achieves state-of-the-art performance under various scenarios such as different depth ranges, virtual and real, different weather conditions, and the few-shot incremental learning setting on 12 datasets. We will release the source codes and pre-trained models.