We propose SUB-Depth, a universal multi-task training framework for self-supervised monocular depth estimation (SDE). Depth models trained with SUB-Depth outperform the same models trained in a standard single-task SDE framework. By introducing an additional self-distillation task into a standard SDE training framework, SUB-Depth trains a depth network, not only to predict the depth map for an image reconstruction task, but also to distill knowledge from a trained teacher network with unlabelled data. To take advantage of this multi-task setting, we propose homoscedastic uncertainty formulations for each task to penalize areas likely to be affected by teacher network noise, or violate SDE assumptions. We present extensive evaluations on KITTI to demonstrate the improvements achieved by training a range of existing networks using the proposed framework, and we achieve state-of-the-art performance on this task. Additionally, SUB-Depth enables models to estimate uncertainty on depth output.
翻译:我们提出“分司”是一个通用的多任务培训框架,用于自我监督单层深度估算(SDE); 由分司培训的深度模型优于在标准单一任务SDE框架内培训的同一模型; 通过在标准SDE培训框架中引入额外的自我提炼任务,分司培训了一个深度网络,不仅用于预测图像重建任务的深度地图,而且用于从受过训练的教师网络中提取无标签数据的知识; 为了利用这一多任务设置,我们建议为每项任务提出同质不确定性配方,以惩罚可能受教师网络噪音影响或违反SDE假设的地区; 我们对KITTI进行广泛的评价,以展示通过利用拟议框架培训一系列现有网络而取得的改进,我们实现了这项任务的先进业绩。此外,分司还使模型能够估计深度输出的不确定性。