With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to lifelong learning scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: i) developing a model capable of addressing the depth scale variation through scale-aware depth learning, ii) devising an effective learning strategy to handle significant domain gaps, and iii) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this paper, we present i) a lightweight multi-head framework that effectively tackles the depth scale imbalance, ii) an uncertainty-aware lifelong learning solution that adeptly handles significant domain gaps, and iii) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8% to 15%.

翻译：随着自动驾驶和机器人导航的快速发展，对能够估计度量（绝对）深度的终身学习模型的需求日益增长。终身学习方法在模型训练、数据存储和采集方面具有显著的潜在成本节约优势。然而，RGB图像和深度图的质量依赖于传感器，且现实世界中的深度图表现出领域特定特征，导致深度范围存在差异。这些挑战将现有方法局限于领域间隙较小且仅能估计相对深度图的终身学习场景。为促进终身度量深度学习，我们识别出三个需要关注的关键技术挑战：i) 开发能够通过尺度感知深度学习解决深度尺度变化的模型，ii) 设计有效的学习策略以处理显著领域间隙，以及iii) 为实际应用中的领域感知深度推理创建自动化解决方案。基于上述考量，本文提出：i) 一个有效应对深度尺度不平衡的轻量级多头框架，ii) 一种能够巧妙处理显著领域间隙的不确定性感知终身学习方案，以及iii) 一种用于实时推理的在线领域特定预测器选择方法。通过大量数值研究，我们证明所提方法能够实现良好的效率、稳定性和可塑性，在基准测试中领先8%至15%。