  Spatial scene understanding, including monocular depth estimation, is an
important problem in various applications, such as robotics and autonomous
driving. While improvements in unsupervised monocular depth estimation have
potentially allowed models to be trained on diverse crowdsourced videos, this
remains underexplored as most methods utilize the standard training protocol,
wherein the models are trained from scratch on all data after new data is
collected. Instead, continual training of models on sequentially collected data
would significantly reduce computational and memory costs. Nevertheless, naive
continual training leads to catastrophic forgetting, where the model
performance deteriorates on older domains as it learns on newer domains,
highlighting the trade-off between model stability and plasticity. While
several techniques have been proposed to address this issue in image
classification, the high-dimensional and spatiotemporally correlated outputs of
depth estimation make it a distinct challenge. To the best of our knowledge, no
framework or method currently exists focusing on the problem of continual
learning in depth estimation. Thus, we introduce a framework that captures the
challenges of continual unsupervised depth estimation (CUDE), and define the
necessary metrics to evaluate model performance. We propose a rehearsal-based
dual-memory method, MonoDepthCL, which utilizes spatiotemporal consistency for
continual learning in depth estimation, even when the camera intrinsics are
unknown.
