Learning Scribbles for Dense Depth: Weakly Supervised Single Underwater Image Depth Estimation Boosted by Multitask Learning
Kunqian Li, Xiya Wang, Wenjie Liu, Qi Qi, Guojia Hou, Zhiguo Zhang, Kun Sun
Abstract
Estimating depth from a single underwater image is one of the main tasks of underwater visual perception. However, data-driven underwater depth estimation methods have long been challenging to make breakthroughs due to the difficulty of obtaining a large number of true-value references. This is partly due to the high cost of acquisition equipment, which is difficult to be applied to diverse ocean scenes by a wide range of users, and therefore sample diversity is difficult to guarantee; on the other hand, manual annotation of dense depth relationships is almost impossible to achieve. In this paper, we establish a new underwater relative depth estimation benchmark, namely SUIM-SDA, by extending the SUIM dataset with more than 6,000 manually annotated depth trendlines, 25 million pixels with paired depth-ranking labels and 14 million depth-ranked pixel pairs. Using the sparse depth relation annotation provided by SUIM-SDA and the semantic information provided by SUIM, we design a new multi-stage multi-task learning framework to predict a dense relative depth map for a single underwater image. Comprehensive comparison and ablation study on the publicly available dataset and our new benchmark demonstrate the effectiveness of the proposed weakly-supervised strategy for dense relative depth estimation. The new benchmark, source code, and trained models are available on the project home page: https://wangxy97.github.io/WsUIDNet.